Sources Of Model Error Research Articles

Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles

Inaccuracies in modeling of the geometric shape of PAMs has long been cited as a probable source of error in modeling and design efforts. The geometric shape and volume of PAMs is commonly approximated using a cylindrical shape profile, even though its shape is non-cylindrical. Correction factors—based on qualitative observations of the PAM’s general shape—are often implemented to compensate for error in this cylindrical shape approximation. However, there is little evidence or consensus on the accuracy and form of these correction factors. Approximations of the shape profile are also used to calculate the internal volume of PAMs, as experimental measurements of the internal volume require intrusive testing methods and specialized equipment. This research presents a photogrammetric method for measuring the shape profile and internal volume of PAMs. A test setup, method of image data acquisition, and a preliminary analysis of the image data, is presented in this research. A 22.2 mm (7/8 in) diameter PAM is used to demonstrate the photogrammetric procedure and test its accuracy. Analysis of the tested PAM characterizes trends of the shape profile with respect to pressure and contraction. The common method of estimating the diameter—through the use of the cylindrical approximation and initial geometry of the PAM—is tested by comparison to the measured shape profile data. Finally, a simple method of calculating the internal volume using the measured shape profile data is developed. The presented method of acquiring photogrammetric measurements of PAM shape produces an accurate characterization of its shape profile, thereby mitigating uncertainty in PAM shape in analysis and other efforts.

Clustering circulation in eastern Asia as a tool for exploring possible mechanisms of extreme events and sources of model error

Every summer in eastern Asia, the combined effects of complex topography, multiple potential moisture sources and a mixture of sub-tropical and mid-latitude dynamics produces an environment, in which subtle differences in spatial patterns of atmospheric circulation can profoundly affect the geographic distribution of rainfall. Understanding and quantifying these patterns is a fundamental requirement if we are to understand, predict or project rainfall events likely to cause impacts to society in the region. To help the scientific community with this task, a method is presented here, in which spatial patterns of summer sea level pressure over eastern Asia are clustered into a set of eight circulation types, with examples given of their potential use. We find that the within-season prevalence of individual circulation types exhibit a strong relationship with the seasonal progression of the summer monsoon whilst providing a spatially coherent, physical interpretation of the monsoon for all parts of eastern Asia. Beyond this, the clustering approach permits the attribution of notable rainfall anomalies in almost any location within the domain used to build the clusters. Examples are given, showing how circulation specific moisture transport anomalies in one part of China can result in anomalously dry days in another part of the country. Two further applications of the clustering approach are demonstrated using climate simulations. The first is of circulation specific model errors which can allow targeted model development. The second provides information about anomalies which are plausible, but have yet to be observed, possibly due to limitations in the observed record.

ВИТОКИ ПОМИЛОК ПРОГНОЗУВАННЯ СИТУАЦІЙ В КОМП’ЮТЕРНИХ СИСТЕМАХ ІМІТАЦІЙНОГО МОДЕЛЮВАННЯ

Modern trends in the development of the theory and practice of scientific research show that one of the main elements of determining the reliability of research results is computer modeling. With the development of high-level programming languages and the availability of technical means, a significant number of researchers are using modeling software as a research tool. With the help of simulation systems, decisions are made that affect human lives, the directions of development of industries and the prospects of the state. Experts in the relevant industries, based on the results of computer calculations, do not take into account the peculiarities of the accuracy of calculations in a computer with binary numbers and the influence of binary arithmetic on the results of simulation. In the article, the authors used an approach to determine the sources of computer modeling errors using fragments of program code. With the help of fragments of the program code the differences of results of decimal and binary calculations with features of representation of binary numbers with a floating point are shown. It was found that the result of calculations is most influenced by errors of number denormalization, cyclicity errors of of sum operation and rounding errors depending on the description of the accuracy of representation of variables directly in the program code. The use of this approach makes it possible to exclude or determine the ranges of numerical values, the representation of which in binary form, can lead to distortion of the simulation results already during the implementation of software products. Keywords: floating point representations, numerical simulation values, mathematical model, computer architecture, simulation errors, rounding errors, denormalization errors.

Stable Isotope Mixing Models Are Biased by the Choice of Sample Preservation and Pre-treatment: Implications for Studies of Aquatic Food Webs

Stable isotope analysis has become one of the most widely used techniques in ecological studies. However, there are still uncertainties about the effects of sample preservation and pre-treatment on the ecological interpretation of stable isotope data and especially on Bayesian stable isotope mixing models. Here, Bayesian mixing models were used to study how three different preservation methods (drying, freezing, formalin) and two pre-treatments (acidification, lipid removal) affect the estimation of the utilized organic matter sources for two benthic invertebrate species (Limecola balthica, Crangon crangon) collected in the Baltic Sea. Furthermore, commonly used mathematical lipid normalization and formalin correction were applied to check if they were able to adjust the model results correctly. Preservation effects were strong on model outcomes for frozen as well as formalin preserved L. balthica samples, but not for C. crangon. Pre-treatment effects varied with species and preservation method and neither lipid normalization nor mathematical formalin correction consistently resulted in the desired model outcomes. Our analysis highlights that particularly small, not significant changes in stable isotope ratios introduced by different preservation and pre-treatments display a so far unrecognized source of error in stable isotope mixing models. We conclude that mathematical correction of benthic invertebrate stable isotopes data should be avoided for Bayesian mixing models and that previously unaddressed effects of sample preservation (especially those arising from preservation by freezing) have potentially biased our understanding of the utilization of organic matter in aquatic food webs.

DDRP: Real-time phenology and climatic suitability modeling of invasive insects.

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Sedimentation rates in the Baltic Sea: A machine learning approach

Sedimentation rate data are applied in a range of studies such as understanding the sediment carbon budget and accumulation of pollutants in marine sediments. Over many years, sedimentation rate samples have been collected within the Baltic Sea. However, to understand patterns in sedimentation rates more broadly across this sea basin requires converting these point data into continuous spatial predictions. The generation of continuous maps remains problematic and under-studied. This study explores the feasibility of machine learning to estimate sedimentation rates, using 137Cs measurements from the Baltic Sea. A random forest model was applied to predict sedimentation rates based on a range of predictor variables that related to the hydrodynamic regime, bathymetric complexity of the seabed, substrate type and proximity to sediment sources. The accuracy of this prediction was tested against an independent set of sedimentation rate samples that had been withheld from model training. The model was also compared against a simple spatial interpolation to assess whether machine learning produces an improvement on previously applied methods. Overall, the modelling approach explained 41.9% of the variance, far surpassing the spatial interpolation method (4.2% variance explained). This study is a first step towards the spatial prediction of sediment accumulation rates in a repeatable and validated way, but further refinement is desirable to improve the accuracy of the predictions. We discuss the potential sources of model error that could have limited the success of this approach and suggest how some of these could be addressed. Our results indicate that short-term sedimentation rates are highest in small coastal basins, while rates in the deep basins of the Baltic Sea are generally low, thereby seemingly contradicting long held views of the deep-basins as the major depocentres in the Baltic Sea. This apparent contradiction might be attributed to the higher spatial detail of our analysis and differences in the time scales of analysis, indicating that sedimentation patterns in the Baltic Sea might be complex in space and time.

Spoofing Signal Generation Based on Manipulation of Code Delay and Doppler Frequency of Authentic GPS Signal

We propose an efficient spoofing signal generation method that uses the processing results of a global positioning system (GPS) receiver for authentic GPS signals. Conventional methods of generating spoofing signals use expensive GPS simulators because the structures of the spoofing signals must be almost identical to those of the GPS signals. Simulators require GPS ephemeris at a specific time and target position. Subsequently, a complicated process is used to generate navigation data using the ephemeris and model error sources such as the satellite clock bias and ionospheric delay. In contrast, the proposed method can generate spoofing signals for the desired target position without requiring GPS simulators; it does so by adjusting the signal processing results of the receiver. Using the navigation results of the receiver, such as position and velocity, the pseudorange delay and spoofing Doppler frequency between the estimated position of the receiver and the target spoofing position are obtained; these are then applied to shift the signal-tracking results of the receiver to create a new signal for the target spoofing position. Our experimental results indicate that the proposed algorithm can effectively generate spoofing signals with characteristics highly similar to those of authentic GPS signals. In addition, we confirmed that the spoofing signals generated by the proposed method are difficult to be detected using conventional spoofing detection techniques.

ECMWF global coupled atmosphere, ocean and sea-ice dataset for the Year of Polar Prediction 2017\u20132020

The Year Of Polar Prediction (YOPP) dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) contains initial condition and forecast model output from the operational global, coupled numerical weather prediction system. The dataset has been created to support model forecast evaluation, predictability studies and model error analyses over polar areas, which are strongly affected by climate change with yet unknown feedbacks on global circulation. The dataset complements YOPP observation and modeling research activities that represent a key deliverable of the World Meteorological Organization’s Polar Prediction Program. The dataset covers the period from mid-2017 until the end of the MOSAiC field campaign, expected for autumn 2020. Initial conditions and forecasts up to day-15 are included for the atmosphere and land surface for the entire period, and for ocean and sea-ice model components after June 2019. In addition, tendencies from model dynamics and individual physical processes are included for the first two forecast days. These are essential for characterizing the contribution of individual processes to model state evolution and, hence, for diagnosing sources of model error.

The hydraulic conductivity of peat with respect to scaling, botanical composition, and greenhouse gas transport: Mini-aquifer tests from the Red Lake Peatland, Minnesota

Hydraulic conductivity (K) is a key but problematic parameter in groundwater models particularly those that simulate flow in weak, readily deformable media, such as peat deposits. As a result, K represents a critical source of error in models that couple hydrological processes with the carbon balance of peatlands, a globally important source for greenhouse gases. We therefore conducted mini-aquifer tests on two mesoscale bog landforms within the large 1300 km2 Red Lake Peatland of northern Minnesota. These tests offer the dual advantage of determining the fine-scale distribution of K within a large (>900 m3) model domain. In addition, the stress created by a 24 h pumping operation should be capable of mobilizing pools of biogenic gases thoughout a deep peat deposit. The pumping results were monitored by 24 to 38 wells in order to calibrate a 3D finite-volume groundwater model with the aid of PEST (Parameter Estimation Analysis). High K values were determined at a Bog Forest (10−5 to 10−6 m s−1) and Bog Lawn (10−3 to 10−4 m s−1) sites, throughout their deep (>4 m) peat profiles. These tests also detected vertically continuous zones of unexpectedly high or low K values in contrast to the horizontal bedding planes and increasing degree of decomposition with depth. The vertical K zones are suggestive of three different modes of bubble transport that either locally dilate or partially block the peat pores. In addition, the tests provided new insights on a conceptual model linking K to the development of all large (>20 km2) forested bog complexes in mid-continental boreal North America.

Multifluids for Representing Subgrid‐Scale Convection

AbstractTraditional parameterizations of convection are a large source of error in weather and climate prediction models, and the assumptions behind them become worse as resolution increases. Multifluid modeling is a promising new method of representing subgrid‐scale and near‐grid‐scale convection allowing for net mass transport by convection and nonequilibrium dynamics. The air is partitioned into two or more fluids, which may represent, for example, updrafts and the nonupdraft environment. Each fluid has its own velocity, temperature, and constituents with separate equations of motion. This paper presents two‐fluid Boussinesq equations for representing subgrid‐scale dry convection with sinking and w = 0 air in Fluid 0 and rising air in Fluid 1. Two vertical slice test cases are developed to tune parameters and to evaluate the two‐fluid equations: a buoyant rising bubble and radiative convective equilibrium. These are first simulated at high resolution with a single‐fluid model and conditionally averaged based on the sign of the vertical velocity. The test cases are next simulated with the two‐fluid model in one column. A model for entrainment and detrainment based on divergence leads to excellent representation of the convective area fraction. Previous multifluid modeling of convection has used the same pressure for both fluids. This is shown to be a bad approximation, and a model for the pressure difference between the fluids based on divergence is presented.

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview.

Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.

Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty

AbstractSnow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framework. To quantify these effects, we implement a particle filter (PF) assimilation technique to assimilate depth and validate this approach against observed snow density and SWE at 49 snow telemetry sites across 9 years. We sample from continuous in situ snow depth records to test a range of measurement timing and sampling error scenarios representative of remote sensing capabilities. Assimilation reduces density bias by over 40% and SWE bias by over 70% across climate zones and in both wet and dry years. There is little incremental benefit to SWE accuracy when assimilating more than one depth observation near peak accumulation. SWE estimates are less sensitive to observation timing than sampling error. Alternatively, more frequent depth observations improve melt‐out date timing and reduce SWE uncertainty, a key consideration when evaluating the operational utility of DA. In matching depth observations, the PF mostly acts to increase model precipitation inputs, while not systematically shifting other parameter values or forcings across the climate zones represented with the study sites. This demonstrates that precipitation is the largest source of model error. With DA, density errors are still nontrivial (above 10%), illuminating the need for further improvements to modeled density to estimate SWE within specified error limits.

Effects of aerosol dynamics and gas–particle conversion on dry deposition of inorganic reactive nitrogen in a temperate forest

Abstract. Dry deposition has an impact on nitrogen status in forest environments. However, the mechanism for the high dry-deposition rates of fine nitrate particles (NO3-) observed in forests remains unknown and is thus a potential source of error in chemical transport models (CTMs). Here, we modified and applied a multilayer land surface model coupled with dry-deposition and aerosol dynamic processes for a temperate mixed forest in Japan. This represents the first application of such a model to ammonium nitrate (NH4NO3) gas–particle conversion (gpc) and the aerosol water uptake of reactive nitrogen compounds. Thermodynamics, kinetics, and dry deposition for mixed inorganic particles are modeled by a triple-moment modal method. Data for inorganic mass and size-resolved total number concentrations measured by a filter pack and electrical low-pressure impactor in autumn were used for model inputs and subsequent numerical analysis. The model successfully reproduces turbulent fluxes observed above the canopy and vertical micrometeorological profiles noted in our previous studies. The sensitivity tests with and without gpc demonstrated clear changes in the inorganic mass and size-resolved total number concentrations within the canopy. The results also revealed that within-canopy evaporation of NH4NO3 under dry conditions significantly enhances the deposition flux of fine-NO3- and fine-NH4+ particles, while reducing the deposition flux of nitric acid gas (HNO3). As a result of the evaporation of particulate NH4NO3, the calculated daytime mass flux of fine NO3- over the canopy was 15 times higher in the scenario of “gpc” than in the scenario of “no gpc”. This increase caused high contributions from particle deposition flux (NO3- and NH4+) to total nitrogen flux over the forest ecosystem (∼39 %), although the contribution of NH3 was still considerable. A dry-deposition scheme coupled with aerosol dynamics may be required to improve the predictive accuracy of chemical transport models for the surface concentration of inorganic reactive nitrogen.

Evaluation of land-atmosphere processes of the Polar WRF in the summertime Arctic tundra

Abstract Arctic tundra is undergoing a rapid transition due to global warming and will be exposed to snow-free conditions for longer periods under projected climate scenarios. Regional climate modeling is useful for understanding and predicting climate change in the Arctic tundra, however, the lack of in-situ observations of surface energy fluxes and the planetary boundary layer (PBL) structure hinders accurate predictions of local and regional climate around the Arctic. In this study, we investigate the performance of the Polar-optimized version of the Weather Research and Forecasting model (PWRF) in the Arctic tundra on clear days in summer. Based on simultaneous observations of surface fluxes and the PBL structure in Cambridge Bay, Nunavut, Canada, our validation shows that the PWRF simulates a drier environment, leading to a larger Bowen ratio and a warmer atmosphere compared to observations. Further sensitivity analyses indicate that the model biases are mainly from the uncertainties in physical parameters such as surface albedo and emissivity, the solar constant, and the model top height, rather than structural flaws in the model physics. Importantly, the PWRF reproduces the observations more accurately when the observed soil moisture is fed into the simulation. This indicates that there must be improvements in simulations of the land-atmosphere interaction at the Arctic tundra, not only in the accuracy of the initial soil moisture conditions but also in soil hydraulic properties and drainage processes. The mixing diagram analysis also shows that the entrainment process between the PBL and the overlying atmosphere needs to be improved for better weather and climate simulation. Our findings shed light on modeling studies in the Arctic region by disentangling the model error sources from uncertainties by parameters and physics package options.

Numerical methods for entrainment and detrainment in the multi‐fluid Euler equations for convection

AbstractConvection schemes are a large source of error in global weather and climate models, and modern resolutions are often too fine to parametrize convection, but are still too coarse to fully resolve it. Recently, numerical solutions of multi‐fluid equations have been proposed for a more flexible and consistent treatment of subgrid‐scale convection, including net mass transport by convection and non‐equilibrium dynamics. The technique involves splitting the atmosphere into multiple fluids. For example, the atmosphere could be divided into buoyant updraughts and stable regions. The fluids interact through a common pressure, drag and mass transfers (entrainment and detrainment). Little is known about the numerical properties of mass transfer terms between the fluids. We derive mass transfer terms which relabel the fluids and derive numerical properties of the transfer schemes, including boundedness, momentum conservation and energy conservation on a co‐located grid. Numerical simulations of the multi‐fluid Euler equations using a C‐grid are presented using stable and unstable treatments of the transfers on a well‐resolved two‐fluid dry convection test case. We find two schemes which are conservative, stable and bounded for large time steps, and maintain their numerical properties on staggered grids.

Discussion of “Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach” by Saeid Mehdizadeh, Farshad Fathian, Mir Jafar Sadegh Safari and Jan F. Adamowski

This discussion extends published findings on the use of artificial intelligence models for time series modeling/monthly streamflow forecasting at the Port Elgin station on the Saugeen River, Canada. Published results are applied and run in autoregressive (AR) and moving average (MA) models as well as hybrids of these with autoregressive conditional heteroscedasticity (ARCH), namely AR-ARCH and MA-ARCH. The hybrid solutions are concluded to be superior to both linear and nonlinear modeling approaches. However, common nonlinear methods including neural networks have a recognized defect in time series forecasting known as “inappropriate time series modeling inputs”. The present study addresses this significant source of error in nonlinear modeling by referring to time series components via suitable time series preprocessing. Of particular interest in this discussion provides the novel vision for time series modeling using nonlinear approaches. The nature of the hydrological variables in time series modeling has great impact on the predicted output and should thus be considered in the modeling procedure. An appropriate preprocessing technique must also be considered carefully in order to attain reliable nonlinear modeling results.

Validation of Models for the Calculation of Sun Positions and mapped Radiation on inclined Surfaces

In order to validate building simulation programs and to identify sources of errors in implemented models, it is necessary to evaluate individual physical effects step by step. For building simulations, the radiation load that hits buildings is a significant boundary condition. In order to reproduce these loads as accurately as possible, sun position models and weather data adapted to them are necessary. With the help of sun position models, the radiation data (usually available in horizontal or normal radiation) are mapped onto inclined and oriented surfaces. This paper examines a possible validation. For the analysis, different building simulation tools (NANDRAD, Radiance, IDA ICE, Modelica, TRNSYS, ETU Simulation) are compared with different time steps for specific dates using the result data azimuth and altitude. The locations are distributed over the northern and southern hemispheres, inside and outside the tropics and polar circles, and with large deviations from the standard meridian, thus allowing errors to be found in the implemented sun position models. In the following step, these building simulation platforms are compared with regard to their minutely radiation loads on building façades. Errors in the radiation mapping on inclined surfaces are hereby determined.

Study on Chip Reliability Modeling Based on Mutually Dependent Competing Failure of Solder Joints in Different Failure Modes

The chip is a core functional component. Its reliability plays a vital role in electronic equipment normal operation. As the typical cause for chip malfunction, the solder joint degradation is selected to study chip reliability. The degradation models of solder joint in different failure modes are established through data-driven and failure physical model, and chip reliability model is constructed based on mutually competing failures of multiple solder joints. First, the chip reliability degradation test and finite element modeling(FEM) are carried out under coupled environment stress. The solder joint failure modes and degradation processes are studied through the analysis of test data, microstructure and mechanical simulation. Then, solder joint degradation models are established based on Coffin-Manson and Paris functions that have been modified by a data-driven method. Taking the solder joint failure time of degradation model as the characteristic parameter of Weibull distribution, the solder joint reliability function is obtained. Finally, mutually dependent competing failure theory is cited to describe the correlation about solder joint reliability of different failure modes, then the chip reliability model is established. The parameter estimation is realized by the inference function for margins (IFM) method. From verification tests, results show models are highly consistent with the actual reliability, indicting our reliability modeling method achieves the transition from underlying solder joint level to integrated component level. The joint application of different models can make up for the deficiencies of the single model and obtain more accurate results. In addition, we determined that intermetallic compounds (IMC) are main source of model error.

CLUES model calibration: residual analysis to investigate potential sources of model error

ABSTRACT This paper presents the results of the latest calibrations of the CLUES model for each of total nitrogen, total phosphorus and Escherichia coli. The model provides estimates of in-stream annual loads of these contaminants for every river reach in the New Zealand River Environments Classification and has been used in New Zealand for catchment planning and policy development. CLUES has been calibrated nationally against loads estimated from data collected from water quality monitoring stations located across the country which could lead to parameter bias. To ascertain whether there is any systematic bias, the calibration residuals were evaluated against regional (geographical) location and a range of upstream catchment characteristics, namely land use, soil drainage properties, slope, network order, proportion of baseflow in total stream flow, climate, geology and source of flow. We found that CLUES gives reasonable load estimates at the catchment scale (Nash-Sutcliff efficiencies >0.8 for all the contaminants). However, there was significant uncertainty in the parameterisation. While several significant relationships were found between upstream catchment characteristics and the model residuals, these relationships were weak and are unlikely to point to any systematic bias in the calibration.

Heterocyclic Product Formation in Aqueous Brown Carbon Systems

Brown carbon in aerosol remains a significant source of error in global climate modeling due to its complex nature and limited product characterization. Though significant efforts have been made in the previous decade to identify the major light-absorbing brown carbon chromophores formed through the reactions of carbonyl-containing compounds with ammonium, substantial work is still required to identify the main absorbing species resulting from reactions of glyoxal, glycolaldehyde, and hydroxyacetone with ammonium sulfate (AS). Using tandem mass spectrometry and 15N experiments to confirm proposed structures and support their mechanistic pathways, compelling evidence is provided for the formation of pyrazines and imidazoles in the glyoxal + AS, glycolaldehyde + AS, and hydroxyacetone + AS systems. Through density functional theory calculations, the N-containing oligomers and aromatic heterocycles formed within these reaction systems are shown to contribute to brown carbon light absorption, thus holding sig...