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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The seasonal variation of water vapor and ozone in the upper mesosphere - Implications for vertical transport and ozone photochemistry

This paper reviews the data base supplied by ground-based microwave measurements of water vapor in the mesosphere obtained in three separate experiments over an eight-year period. These measurements indicate that the seasonal variation of water vapor in the mesosphere is dominated by an annual component with low values in winter and high values in summer, suggesting that the seasonal variation of water vapor in the mesosphere (below 80 km) is controlled by advective rather than diffusive processes. Both the seasonal variation and the absolute magnitude of the water vapor mixing ratios obtained in microwave measurements were corroborated by measurements obtained in the Spacelab GRILLE and ATMOS experiments, and were found to be consistent with several recent mesospheric dynamics studies.

Bevilacqua, Richard M.↗
NASA NTRS2011

Reactive Nitrogen, Ozone and Ozone Production in the Arctic Troposphere and the Impact of Stratosphere-Troposphere Exchange

We analyze the aircraft observations obtained during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellite (ARCTAS) mission together with the GEOS-5 CO simulation to examine O3 and NOy in the Arctic and sub-Arctic region and their source attribution. Using a number of marker tracers and their probability density distributions, we distinguish various air masses from the background troposphere and examine their contribution to NOx, O3, and O3 production in the Arctic troposphere. The background Arctic troposphere has mean O3 of approximately 60 ppbv and NOx of approximately 25 pptv throughout spring and summer with CO decreases from approximately 145 ppbv in spring to approximately 100 ppbv in summer. These observed CO, NOx and O3 mixing ratios are not notably different from the values measured during the 1988 ABLE-3A and the 2002 TOPSE field campaigns despite the significant changes in the past two decades in processes that could have changed the Arctic tropospheric composition. Air masses associated with stratosphere-troposphere exchange are present throughout the mid and upper troposphere during spring and summer. These air masses with mean O3 concentration of 140-160 ppbv are the most important direct sources of O3 in the Arctic troposphere. In addition, air of stratospheric origin is the only notable driver of net O3 formation in the Arctic due to its sustainable high NOx (75 pptv in spring and 110 pptv in summer) and NOy (approximately 800 pptv in spring and approximately 1100 pptv in summer) levels. The ARCTAS measurements present observational evidence suggesting significant conversion of nitrogen from HNO3 to NOx and then to PAN (a net formation of approximately 120 pptv PAN) in summer when air of stratospheric origin is mixed with tropospheric background during stratosphere-to-troposphere transport. These findings imply that an adequate representation of stratospheric O3 and NOy input are essential in accurately simulating O3 and NOx photochemistry as well as the atmospheric budget of PAN in tropospheric chemistry transport models of the Arctic. Anthropogenic and biomass burning pollution plumes observed during ARCTAS show highly elevated hydrocarbons and NOy (mostly in the form of NOx and PAN), but do not contribute significantly to O3 in the Arctic troposphere except in some of the aged biomass burning plumes sampled during spring. Convection and/or lightning influences are negligible sources of O3 in the Arctic troposphere but can have significant impacts in the upper troposphere in the continental sub-Arctic during summer.

Liang, Q.↗
NASA NTRSDate not supplied

Evaluating Diurnal Ozone Emissions: A Comparative Analysis of DSCOVR EPIC, PANDORA, and TOLNet Data for Space-Based Monitoring Assessment: A Case Study by ASDC

Stratospheric ozone occurs naturally in the upper atmosphere, forming a protective layer that shields us from the sun's harmful ultraviolet rays. Tropospheric ozone is not emitted directly into the air but is created by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC). This reaction happens when pollutants emitted by cars, power plants, industrial boilers, refineries, chemical plants, and other sources chemically react in sunlight [1]. Therefore, increased levels of tropospheric ozone indicate the presence of pollutants in the air. While daily anthropogenic activity causes an increase of ground-level ozone, variation of stratospheric ozone changes happens much slower, so that variation of the total ozone column reflects the variation of the tropospheric column due to natural, e.g., wildfires and anthropogenic air pollution. Both total and tropospheric ozone column products are used in this study. The reflectance spectra measured by the Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) spacecraft are compared with a set of radiative transfer-derived lookup tables for the EPIC filter transmission functions and a wide range of ozone values to retrieve ozone with a maximum resolution of 18 km at the sub-satellite point [2]. EPIC provides total column ozone in level 2 and level 4 products and tropospheric column ozone in level 4 products. Both EPIC Ozone products [2] are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center [3, 4]. Pandora spectrometer instrument measures columnar amounts of trace gases in the atmosphere. These gases (O3, NO2, CH2O) absorb light from the sun at specific wavelengths in the ultraviolet-visible spectrum [5]. Using the theoretical solar spectrum as a reference, Pandora determines trace gas amounts using differential optical absorption spectroscopy (DOAS). Pandora ozone retrievals are available from the Pandonia Global Network [6]. Pandora data from North American major metropolitan areas, New York, NY, Washington DC, Los Angeles, CA, and Mexico City. Tropospheric Ozone Lidar Network (TOLNet) was established in 2012 to provide high spatiotemporal observations of tropospheric ozone to (1) better understand physical processes driving the ozone budget in various meteorological and environmental conditions and (2) validate the tropospheric ozone measurements of space-borne missions [7]. TOLNet data are available at ASDC [8]. While EPIC provides global coverage of ozone retrievals several times daily, temporal resolution may miss some features in daily ozone variations. Ground-based sensors such as Pandora spectrometers and TOLNet lidars provide better temporal resolution while missing continuous spatial coverage. The forthcoming ozone retrieval from the TEMPO mission [9] will provide better spatial and temporal coverage of air quality (including ozone) over North America. This study investigates whether EPIC ozone products can detect diurnal air quality variations and compare ozone temporal development with retrieval by ground-based instruments.

diurnal ozone emissions, DSCOVR EPIC, PANDORA, spa↗
NASA NTRS2020

Stratospheric Impact on the Northern Hemisphere Winter and Spring Ozone Interannual Variability in the Troposphere

In this study we use ozone and stratospheric ozone tracer simulations from the high-resolution (0.5°x0.5°) Goddard Earth Observing System, Version 5 (GEOS-5) in a replay mode to study the impact of stratospheric ozone on tropospheric ozone interannual variability (IAV). We use these simulations in conjunction with ozone sonde measurements from 1990 to 2016 during the winter and spring seasons. The simulations include a stratospheric ozone tracer (StratO3) to aid 15in the evaluation of the impact of stratospheric ozone IAV on the IAV of tropospheric ozone at different altitudes and location. The model is in good agreement with the observed interannual variation of tropospheric ozone, except for the post-Pinatubo period(1992-1994)over the region of North America. Ozone sonde data show a negative ozone anomaly in 1992-1994 following the Pinatubo eruption, with recovery thereafter. The simulated anomaly is only half the magnitude of that observed. Our analysis suggests that the simulated Stratosphere-troposphere exchange (STE)flux deduced from the analysis 20might be too strong over the North American (50°N-70°N) region after the Mt. Pinatubo eruption in the early 1990s,maskingthe impact of lower stratospheric ozone concentration on tropospheric ozone. European ozone sonde measurements show a similar but weaker ozone depletion after the Mt. Pinatubo eruption, which is fully reproduced by the model. Analysis based on the stratospheric ozone tracer (StratO3) identifies differences in strength and vertical extent of stratospheric ozone impact on the tropospheric ozone interannual variation (IAV)between North America and Europe. Over NorthAmerica25stations, the StratO3IAV has a significant impact on tropospheric ozone from the upper to lower troposphere and explains about 60% and 66% of the simulated ozone IAV at 400 hPa, and ~11% and 34% at 700 hPa in winter and spring respectively. Over European stations, the influence is limited to the middle to upper troposphere, and becomes much smaller at 700 hPa. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)assimilated fields exhibit strong longitudinal variations over northern hemisphere (NH) mid-high latitudes, with lower tropopause height and 30lower geopotential height over North America than Europe. These variations associated with the relevant variations in the location of tropospheric jet flows are responsible for the longitudinal differences in the stratospheric ozone impact, with stronger effects over North America than over Europe

tropospheric ozone IAV, Model, ozonesonde, stratos↗
NASA NTRS2006

On the Quality of the Nimbus 7 LIMS Version 6 Ozone for Studies of the Middle Atmosphere

The Nimbus 7 Limb Infrared Monitor of the Stratosphere (LIMS) radiance profile dataset of 1978/79 was reconditioned and reprocessed to Version 6 (V6) profiles of temperature and species that are improved significantly over those from Version 5 (V5). The LIMS V6 dataset was archived for public use in 2002. Improvements for its ozone include: (1) a more accurate accounting for instrument and spacecraft motion effects in the radiances, (2) the use of better spectroscopic line parameters for its ozone forward model, (3) retrievals of all its scans, (4) more accurate and compatible temperature versus pressure profiles (or T(p)), which are needed for the registration of the ozone radiances and for the removal of temperature effects from them, and (5) a better accounting for interfering species in the lower stratosphere. The retrieved V6 ozone profiles extend from near cloud top altitudes to about 80 km and from 64S to 84N latitude with better sampling along the orbit than for the V5 dataset. Calculated estimates of the single-profile precision and accuracy are provided for the V6 ozone from this study. Precision estimates based on the data themselves are of order 3% or better from 1 to 30 hPa. Estimates of total systematic error for a single profile are hard to generalize because the separate sources of error may not all be of the same sign and they depend somewhat on the atmospheric state. It is estimated that the V6 zonal mean ozone distributions are accurate to within 9% to 7% from 50 hPa to 3 hPa, respectively. Effects of a temperature bias can be significant and may be present at 1 to 2 hPa though. There may be ozone biases of order 10% at those levels due to possible biases of up to +2 K, but there is no indication of a similar problem elsewhere in the stratosphere. Simulation studies show that the LIMS retrievals are also underestimating slightly the small amplitudes of the atmospheric temperature tides, which affect its retrieved day/night ozone differences. There are small biases in the middle to lower stratosphere for the ascending versus descending node LIMS ozone, due principally to not accounting for the asymmetric weighting of its radiances across the tangent layer. The estimates of total accuracy were assessed by comparing the daily zonal mean LIMS ozone distributions against those from the Nimbus 7 SBUV Version 8 (V8) dataset for the same period. Generally, the LIMS V6 ozone agrees well with SBUV, except perhaps in the tropical lower stratosphere where the LIMS ozone is less. Still, the accuracy for LIMS V6 ozone in the lower stratosphere is improved over that found for LIMS V5, as indicated by several LIMS comparisons with ECC ozonesonde profiles. The LIMS V6 ozone is considered especially suitable for detailed studies of large-scale stratospheric processes above the 100-hPa level. Comparison of diurnal, photochemical model calculations with the monthly-averaged, upper stratospheric ozone observed from LIMS V6 indicates only a slight ozone deficit for the model at about 2 hPa. However, that deficit exhibits little to no seasonal variation and is in good agreement with similar model comparisons for a seasonal time series of ozone obtained with ground-based microwave instruments. Because the LIMS V6 ozone in the lower stratosphere has improved accuracy and sampling versus that of V5, it should now be possible to conduct quantitative studies of ozone transport and chemistry for the northern hemisphere, polar stratospheric winter of 1978/79 a time period when the catalytic loss of ozone due to reactive chlorine should not have been a major factor for the Arctic region.

Remsberg, Ellis↗
NASA NTRS2003

Monitoring of Observation Errors in the Assimilation of Satellite Ozone Data

The stratospheric ozone layer protects life on Earth from the harmful effects of solar ultravioiet radiation. The ozone layer is currently in a fragile state because of depletion caused by man-made chemicals, especially chlorofluorocarbons. The state of the ozone layer is being monitored and evaluated by scientific experts around the world, in order to help policy makers assess the impacts of international protocols that control the production and release of ozone depleting chemicals. Scientists use a variety ozone measurements and models in order to form a comprehensive picture about the current state of the ozone layer, and to predict the future behavior (expected to be a recovery, as the abundance of the depleting chemicals decreases). Among the data sets used, those from satellite-borne instruments have the advantage of providing a wealth of information about the ozone distribution over most of the globe. Several instruments onboard American and international satellites make measurements of the properties of the atmosphere, from which atmospheric ozone amounts are estimated; long-term measurement programs enable monitoring of trends in ozone. However, the characteristics of satellite instruments change in time. For example, the instrument lenses through which measurements are made may deteriorate over time, or the satellite orbit may drift so that measurements over each location are made later and later in the day. These changes may increase the errors in the retrieved ozone amounts, and degrade the quality of estimated ozone amounts and of their variability. Our work focuses on combining the satellite ozone data with global models that capture atmospheric motion and ozone chemistry, using advanced statistical techniques: this is known as data assimilation. Our method provides a three-dimensional global ozone distribution that is consistent with both the satellite measurements and with our understanding of processes (described in the models) that control ozone distribution. Through the monitoring of statistical properties of the agreement between the data and the model, this approach also enables us to detect changes in the quality of ozone data retrieved from satellite-borne instrument measurements. This paper demonstrates that calculations of the changes in satellite data quality, and the impact these changes on the estimates of the global ozone distribution, can assist in maintaining the uniform quality of the satellite ozone data throughout the lifetime of these instruments, thus contributing to our understanding of long-term ozone change.

Stajner, Ivanka↗