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Observation and Modeling of the Solar Transition Region: Multi-Spectral Solar Telescope Array Observations - 1
We report on observations of the solar atmosphere in several extreme-ultraviolet and far-ultraviolet bandpasses obtained by the Multi-Spectral Solar Telescope Array, a rocket-borne spectroheliograph, on flights in 1987, 1991, and 1994, spanning the last solar maximum. Quiet-Sun emission observed in the 171-175 Angstrom bandpass, which includes lines of O v, O VI, Fe IX, and Fe X, has been analyzed to test models of the temperatures and geometries of the structures responsible for this emission. Analyses of intensity variations above the solar limb reveal scale heights consistent with a quiet-Sun plasma temperature of 500,000 less than or equal to T (sub e) less than or equal to 800,000 K. The structures responsible for the quiet-Sun EUV emission are modeled as small quasi-static loops. We submit our models to several tests. We compare the emission our models would produce in the bandpass of our telescope to the emission we have observed. We find that the emission predicted by loop models with maximum temperatures between 700,000 and 900,000 K are consistent with our observations. We also compare the absolute flux predicted by our models in a typical upper transition region line to the flux measured by previous observers. Finally, we present a preliminary comparison of the predictions of our models with diagnostic spectral line ratios from previous observers. Intensity modulations in the quiet Sun are observed to occur on a scale comparable to the supergranular scale. We discuss the implications that a distribution of loops of the type we model here would have for heating the local network at the loops' footpoints.
Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation
An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.
Flare heating by energetic nonthermal electrons
A model has been developed which includes, in a self-consistent fashion, the energy degradation and attendant bremsstrahlung emission of a high-energy electron beam, heating of a flare plasma by the beam, and subsequent cooling by thermal conduction and radiation. To assist in the interpretation of experimental diagnostics, the model also characterizes the radiative behavior of iron ions present in the flare plasma. Electron deposition is described by the Fokker-Planck equation for an initial power-law particle distribution. Results are presented for the bremsstrahlung radiation emitted by the incident beam as it impinges on the disturbed atmosphere. A comparison is made between direct beam heating and thermal-conduction heating of the flare plasma. The radiation emitted by several selected spectral lines of Fe ions is determined as a function of time during and after deposition
Dynamic Adjustment of Simulation Parameters for Efficient Vehicle Prognostics
Model-based diagnostics and prognostics rely on state estimation and uncertainty management algorithms to produce useful information for system operators and maintainers. This information enables more informed operational decisions, condition-based maintenance, and overall mission safety assurance. Typically, uncertainty is associated with vehicle state-of-health estimation and prediction results because of modeling errors, internal or external sources of noise, and sensor inaccuracy. Probabilistic uncertainty management methods including Sequential Monte Carlo simulation are commonly used to reason about state-of-health estimates and predictions in the presence of these sources of uncertainty. However, such algorithms can be computationally expensive as they require a very large number of samples to obtain a sufficiently accurate quantification of the end of life probability distribution. As a result, highly mobile autonomous systems that leverage the prognostic results for mission-level replanning are often constrained in their processing capability because of these computationally expensive simulation approaches. Therefore, in this paper, we investigate algorithmic methods for dynamically adjusting simulation time step as well as number of samples to achieve highly efficient prognostic results while maintaining results accuracy. Results obtained from simulated flight experiments of an electric unmanned aerial vehicle are presented to verify the efficacy of such algorithms.
Diagnosing the Sensitivity of Local Land-Atmosphere Coupling via the Soil Moisture-Boundary Layer Interaction
The inherent coupled nature of earth s energy and water cycles places significant importance on the proper representation and diagnosis of land atmosphere (LA) interactions in hydrometeorological prediction models. However, the precise nature of the soil moisture precipitation relationship at the local scale is largely determined by a series of nonlinear processes and feedbacks that are difficult to quantify. To quantify the strength of the local LA coupling (LoCo), this process chain must be considered both in full and as individual components through their relationships and sensitivities. To address this, recent modeling and diagnostic studies have been extended to 1) quantify the processes governing LoCo utilizing the thermodynamic properties of mixing diagrams, and 2) diagnose the sensitivity of coupled systems, including clouds and moist processes, to perturbations in soil moisture. This work employs NASA s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) mesoscale model and simulations performed over the U.S. Southern Great Plains. The behavior of different planetary boundary layers (PBL) and land surface scheme couplings in LIS WRF are examined in the context of the evolution of thermodynamic quantities that link the surface soil moisture condition to the PBL regime, clouds, and precipitation. Specifically, the tendency toward saturation in the PBL is quantified by the lifting condensation level (LCL) deficit and addressed as a function of time and space. The sensitivity of the LCL deficit to the soil moisture condition is indicative of the strength of LoCo, where both positive and negative feedbacks can be identified. Overall, this methodology can be applied to any model or observations and is a crucial step toward improved evaluation and quantification of LoCo within models, particularly given the advent of next-generation satellite measurements of PBL and land surface properties along with advances in data assimilation schemes.
Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems
The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.
A first-order global model of Late Cenozoic climatic change: Orbital forcing as a pacemaker of the ice ages
The development of a theory of the evolution of the climate of the earth over millions of years can be subdivided into three fundamental, nested, problems: (1) to establish by equilibrium climate models (e.g., general circulation models) the diagnostic relations, valid at any time, between the fast-response climate variables (i.e., the 'weather statistics') and both the prescribed external radiative forcing and the prescribed distribution of the slow response variables (e.g., the ice sheets and shelves, the deep ocean state, and the atmospheric CO2 concentration); (2) to construct, by an essentially inductive process, a model of the time-dependent evolution of the slow-response climatic variables over time scales longer than the damping times of these variables but shorter than the time scale of tectonic changes in the boundary conditions (e.g., altered geography and elevation of the continents, slow outgassing, and weathering) and ultra-slow astronomical changes such as in the solar radiative output; and (3) to determine the nature of these ultra-slow processes and their effects on the evolution of the equilibrium state of the climatic system about which the above time-dependent variations occur. All three problems are discussed in the context of the theory of the Quaternary climate, which will be incomplete unless it is embedded in a more general theory for the fuller Cenozoic that can accommodate the onset of the ice-age fluctuations. We construct a simple mathematical model for the Late Cenozoic climatic changes based on the hypothesis that forced and free variations of the concentration of atmospheric greenhouse gases (notably CO2), coupled with changes in the deep ocean state and ice mass, under the additional 'pacemaking' influence of earth-orbital forcing, are primary determinants of the climate state over this period. Our goal is to illustrate how a single model governing both very long term variations and higher frequency oscillatory variations in the Pleistocene can be formulated with relatively few adjustable parameters.
Development of a Model Based Technique for Gear Diagnostics using the Wigner-Ville method
Imperfections in gear tooth geometry often result from errors in the manufacturing process or excessive material wear during operation. Such faults in the gear tooth geometry can result in large vibrations in the transmission system, and, in some cases, may lead to early failure of the gear transmission system. This report presents the study of the effects of imperfection in gear tooth geometry on the dynamic characteristics of a gear transmission system. The faults in the gear tooth geometry are modeled numerically as the deviation of the tooth profile from its original involute geometry. The changes in gear mesh stiffness due to various profile and pattern variations are evaluated numerically. The resulting changes in the mesh stiffness are incorporated into a computer code to simulate the dynamics of the gear transmission system. A parametric study is performed to examine the sensitivity of gear tooth geometry imperfections on the vibration of a gear transmission system. The parameters variations in this study consist of the magnitude of the imperfection, the pattern of the profile variation, and the total number of teeth affected. Numerical results from the dynamic simulations are examined in both the time and the frequency domains. A joint time-frequency analysis procedure using the Wigner-Ville Distribution is also introduced to identify the location of the damaged tooth from the vibration signature. Numerical simulations of the system dynamics with gear faults were compared to experimental results. An optimal tracker was introduced to quantify the level of damage in the gear mesh system. Conclusions are drawn from the results of this numerical study.
Temporal Consistency Between Gross Primary Production and Solar-Induced Chlorophyll Fluorescence in the Ten Most Populous Megacity Areas over Years
The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5deg x 0.5deg resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.
Strain-Rate-Free Diffusion Flames: Initiation, Properties, and Quenching
For about a half century, the stabilization of a steady planar deflagration on a heat-sink-type flat-flame burner has been of extraordinary service for the theoretical modeling and diagnostic probing of combusting gaseous mixtures. However, most engineering devices and most unwanted fire involve the burning of initially unmixed reactants. The most vigorous burning of initially separated gaseous fuel and oxidizer is the diffusion flame. In this useful idealization (limiting case), the reactants are converted to product at a mathematically thin interface, so no interpenetration of fuel and oxidizer occurs. This limit is of practical importance because it often characterizes the condition of optimal performance (and sometimes environmentally objectionable operation) of a combustor. A steady planar diffusion flame is most closely approached in the laboratory in the counterflow apparatus. The utility of this simple-strain-rate flow for the modeling and probing of diffusion flames was noted by Pandya and Weinberg 35 years ago, though only in the last decade or so has its use become internationally common place. However, typically, as the strain rate a is reduced below about 20 cm(exp -1), and the diffusion-flame limit (reaction rate much faster than the flow rate) is approached, the burning is observed to become unstable in earth gravity. The advantageous steady planar flow is not available in the diffusion-flame limit in earth gravity. This is unfortunate because the typical spatial scale in a counterflow is (k/a)(sup 1/2), where k denotes a characteristic diffusion coefficient; thus, the length scale becomes large, and the reacting flow is particularly amenable to diagnostic probing, as the diffusion-flame limit is approached. The disruption of planar symmetry is owing the fact that, as the strain rate a decreases, the residence time (l/a) of the throughput in the counterflow burner increases. Observationally, when the residence time exceeds about 50 msec, the inevitably present convective (Rayleigh-Benard) instabilities, associated with hot-under-cold (flame-under-fresh-reactant) stratification of fluid in a gravitational field, have time to grow to finite amplitude during transit of the burner.
Transient Region Coverage in the Propulsion IVHM Technology Experiment
Over the last several years researchers at NASA Glenn and Ames Research Centers have developed a real-time fault detection and isolation system for propulsion subsystems of future space vehicles. The Propulsion IVHM Technology Experiment (PITEX), as it is called follows the model-based diagnostic methodology and employs Livingstone, developed at NASA Ames, as its reasoning engine. The system has been tested on,flight-like hardware through a series of nominal and fault scenarios. These scenarios have been developed using a highly detailed simulation of the X-34 flight demonstrator main propulsion system and include realistic failures involving valves, regulators, microswitches, and sensors. This paper focuses on one of the recent research and development efforts under PITEX - to provide more complete transient region coverage. It describes the development of the transient monitors, the corresponding modeling methodology, and the interface software responsible for coordinating the flow of information between the quantitative monitors and the qualitative, discrete representation Livingstone.
Real-time diagnostics for a reusable rocket engine
A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.
Real-Time Optical Monitoring of Flow Kinetics and Gas Phase Reactions Under High-Pressure OMCVD Conditions
This contribution addresses the real-time optical characterization of gas flow and gas phase reactions as they play a crucial role for chemical vapor phase depositions utilizing elevated and high pressure chemical vapor deposition (HPCVD) conditions. The objectives of these experiments are to validate on the basis of results on real-time optical diagnostics process models simulation codes, and provide input parameter sets needed for analysis and control of chemical vapor deposition at elevated pressures. Access to microgravity is required to retain high pressure conditions of laminar flow, which is essential for successful acquisition and interpretation of the optical data. In this contribution, we describe the design and construction of the HPCVD system, which include access ports for various optical methods of real-time process monitoring and to analyze the initial stages of heteroepitaxy and steady-state growth in the different pressure ranges. To analyze the onset of turbulence, provisions are made for implementation of experimental methods for in-situ characterization of the nature of flow. This knowledge will be the basis for the design definition of experiments under microgravity, where gas flow conditions, gas phase and surface chemistry, might be analyzed by remote controlled real-time diagnostics tools, developed in this research project.
Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 5
The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to
A study of a non-developing easterly wave
Dynamical structure of tropical atmosphere based on experiments with diagnostic numerical model
Observational and theoretical evidence in support of a significant in-situ photochemical source of tropospheric ozone
The latitudinal and seasonal variation of ozone in the troposphere is discussed. Of particular interest is the asymmetrical behavior of this gas with respect to the two hemispheres. These asymmetries, when coupled with a diagnostic photochemical model of the troposphere, lends support to the view that ozone cannot be viewed as an inert tracer of stratospheric origin. In the calculations it is noted that it is likely that the budgets of carbon monoxide and tropospheric ozone may be quite dependent on each other and the calculations are discussed in light of the uncertainty which currently exists about representative global tropospheric background concentrations of the nitrogen oxides. In addition, the seasonal variation of excess (C-14)O2 (a stratospheric tracer) is examined and compared with the seasonal ozone variation during the same period of observations at the same location and altitudes. The distinct maxima for ozone found during the summer in the lower troposphere are not present for the (C-14)O2 data. This finding likewise suggests that photochemical processes taking place in the troposphere are an important source term for tropospheric ozone.