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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth

Abstract The depth of convective clouds affects vertical transport of atmospheric constituents, influencing downstream weather and climate. Atmospheric controls on the maximum depth reached by moist convection are investigated with radar‐tracked convective cells tagged with sounding‐derived atmospheric parameters from a field campaign in central Argentina. Regression analyses show that narrow (<12‐km diameter) and wide (>16‐km diameter) cell depths respond to disparate factors, where cell areas are defined using composite reflectivity signatures. Undiluted lifted parcel indices including convective available potential energy (CAPE) and level of neutral buoyancy (LNB) are top predictors of wide cell maximum depth while mid‐tropospheric relative humidity is the top predictor of narrow cell maximum depth. Because narrow cells are more numerous than wide cells, the overall outcome of the full cell population does not strongly correlate with CAPE and LNB conditions. Tracked cells and atmospheric conditions in a simulation with 3‐km grid spacing covering the field campaign produce similar results to those observed. Narrow cells that are relatively deep have a cooler and moister mid‐troposphere with weaker free tropospheric subsidence, while relatively deep wide cells have much warmer and moister lower tropospheric conditions. These atmospheric differences are present 1 hr before cell initiation at both a fixed observing site and variable cell initiation locations. Simulated narrow cell maximum equivalent potential temperature decreases with height at a rate similar to the ambient vertical gradient, causing these cells to fall short of their LNB and supporting the view that entrainment‐driven dilution is a dominant control on their depth.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Studying the 2019 Australian Bushfires Disaster using NASA Data: A Data-Driven Storytelling Approach

The 2019-2020 Australian fire season was particularly devastating, with millions of acres of land burned and impacts affecting Australian ecology, local populations and air quality, and the atmosphere. Australia saw unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. The fires gave rise to a host of atmospheric phenomenon, including smoke transport and lofting generated by storm-induced by fires, known as pyrocumulonimbus, reaching the stratosphere. NASA’s satellites not only tracked the event in real time, but also gathered data to further inform forecasting and response methods in the future. To better assist the public in understanding the lead up, impacts, and aftermath effects of these fires, the Science Outreach Team at NASA Langley Research Center’s Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) used Esri’s storymap tool to guide users through understanding relevant phenomenon, contributing factors, the effects this event has had on global atmospheric composition, and the science behind researching the tie between disasters and public health. The storymap uses data from the ASDC-supported NASA missions Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), the Stratospheric Aerosol and Gas Experiment (SAGE III), and Multi-angle Imaging SpectroRadiometer (MISR). By using data-driven storytelling to communicate impacts of a large fire event, we hope to provide an accessible, engaging science outreach tool format.

Sanjana Paul↗
NASA NTRSDate not supplied

Solar Migrating Diurnal Tide in the Upper Mesosphere and Lower Thermosphere from SD-WACCM-X, Aura/MLS, and TIMED/SABER

Solar heating on the rotating Earth is a fundamental forcing of atmospheric tidal waves. The migrating diurnal tide, propagating from the troposphere to the upper mesosphere and lower thermosphere, is a large global disturbance that drives most of the daily variations in dynamics, thermal structures, chemistry, as well as atmospheric compositions. In this study, we present a comprehensive analysis of the (1,1) propagating diurnal tides derived from multi-year MLS and SABER observations, to characterize and better understand interannual and long-term tidal variability in the upper atmosphere. Although the (1,1) tide is driven by the solar heating, its interannual variations are largely determined by the internal variability of Earth’s climate system. The tidal amplitudes derived from MLS and SABER data agree well with each other in terms of monthly climatology and interannual variations, showing a consistent seasonal cycle and modulations from the Quasi Biennial Oscillation (QBO). The Ensemble Empirical Mode Decomposition (EEMD) analysis, employed to extract the low frequency variations, also reveals an ENSO-like (42 months) influence on tidal amplitudes in the upper mesosphere. While their (1,1) tidal amplitudes are less affected by the solar cycle, the mean mixing ratio of carbon monoxide (CO) and ozone (O3) is significantly modulated by the 11-year solar cycle due to their UV-dependent photochemistry.

mesosphere↗
NASA NTRSDate not supplied

NASA GEOS-CF: Overview, Applications, Future Direction

Since 2019, the NASA Global Earth Observing System (GEOS) model has been used to generate global, near-real-time estimates and daily five-day forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). Because GEOS-CF includes atmospheric levels up through the stratosphere, this system has been leveraged to support the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite mission and provide stratospheric intrusion alerts to ground-based monitoring stations. We will present recent advances to GEOS-CF which includes assimilation of satellite observations to produce more accurate model analyses. We further discuss our future plans for a composition reanalysis.

K. Emma Knowland↗
NASA NTRS1981

Titan on the eve of Voyager encounter

A decade of scientific study of Titan is reviewed with emphasis on the composition and the extent of its atmosphere. Several viable models are briefly discussed including the inversion model of Danielsen et al. (1973), Hunten's model (1978) which includes an extensive troposphere and a warm surface, with nitrogen as the major component, and models with neon, primordial carbon monoxide, or various mixtures of hydrogen, helium, and CH4. Recent observations by Pioneer 11 and other satellites are examined.

Caldwell, J.↗
NASA NTRS2020

The Evolution of Lidar Networks: A US Perspective

Atmospheric aerosols and clouds play an important role in climate by directly scattering and absorbing sunlight. Aerosol-cloud interactions modify the particle properties causing indirect effects, alter aerosol deposition and rainfall, and contribute substantially to the uncertainties in predicting climate effects. Aerosols also affect air quality and both constituents modulate boundary layer dynamics to a certain extent, in turn impacting aerosol transport and aerosol-cloud interactions. The spatiotemporal distribution of aerosol and cloud layers is thus important, and lidar remains the primary instrument for determining the vertical distribution of aerosols and thin clouds. Lidar still provides important information for opaque cloud layers by determining base heights. Combined lidar and radar data provide a comprehensive coverage of aerosol and cloud vertical distributions, and thus locations where and when they interact. Lidar data provide a means of constraining column aerosol loading observations (e.g. AOD) to vertical extents. In addition, lidar has proven effective at determining a proxy for boundary layer height by examining aerosol gradients and/or cloud base heights in lidar profiles. Polarized lidars provide additional information on particle shape, allowing estimates of cloud phase and separation of dust or smoke from sulfate and sea salt aerosols. Given the importance of lidar observations and a critical maturation in technology and retrieval techniques, several organizations began to build operational lidar networks around 2000. The European Aerosol Research Lidar Network (EARLINET) was started as a research focused network of advanced lidars across Europe. The Asian Dust Lidar Network (ADNET) was also developed as a regional network providing lidar profiles of dust and pollution across Eastern Asia. In the US, the NASA Micro Pulse Lidar Network (MPLNET) was created to provide global lidar profiling at key sites in the NASA Aerosol Robotic Network (AERONET). The Network for the Detection of Atmospheric Composition Change (NDACC) pre-dates these networks and many sites have lidar, but it is not strictly a lidar network and at the time focused less on the lower troposphere. Finally, there were already existing ceilometer networks operated by various meteorological agencies, but in particular here in the US the profile data has not been available. The ceilometer networks were used to provide only clouds base heights and estimates of PBL height. Thus, the distinguishing feature between lidar and ceilometer networks was historically the ability to actually provide profile information (in addition to differences in wavelength, and advanced retrievals such as the raman technique). As time progressed, each lidar network matured and developed more operational capabilities and data sets, coupled with viable data centers providing DAAC services and access to near-real-time (NRT) data. In 2008 under WMO guidance, the Global Atmospheric Watch (GAW) Aerosol Lidar Observation Network (GALION) was formed as a global network made up of the existing lidar networks. The goal was to share information, best practices, and develop frameworks and techniques for quality data. GALION grew to include several other regional lidar networks and this has led to a vast increase in quality lidar sites worldwide.

Welton, Ellsworth J.↗
NASA NTRS2020

Using Networked Pandora Observations to Capture Spatiotemporal Changes in Total Column Ozone Associated with Stratosphere-to-Troposphere Transport

Accurately capturing the evolution of episodic stratosphere-to-troposphere transport is critical due to the potential impacts on both climate and air quality. Until now, investigating associated spatiotemporal gradients in total column ozone (TCO) has primarily been the task of observations from polar-orbiting satellites as well as high-resolution models. We explore how a network of five ground-based Pandora spectrometer systems can be utilized in a similar fashion. The passage of a strong mid-latitude cyclone in March 2018 and its associated stratospheric intrusion is used as a case demonstrating the ability of networked Pandora observations to contextualize these regions of transport across space and time. Results show that the high temporal resolution of Pandora observations and the networked approach were able to resolve increases in TCO associated with stratosphere-to-troposphere transport and to capture the spatial context of the chosen episode. The use of networked Pandora observations shows promise for additional transport studies and for supporting future geostationary atmospheric composition satellite missions and modeling efforts.

J. Robinson↗
NASA NTRSDate not supplied

Atmospheric Composition Forecast Model Evaluation Using Ozone Measurements Collected by the Langley Mobile Ozone Lidar

The Langley Mobile Ozone Lidar (LMOL) is a mobile ground based lidar system based at NASA Langley in Hampton, Virginia. Between 2022 and 2024, LMOL collected over 2500 hours of ozone measurements for a range of different atmospheric conditions, including calm days, stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. Here, the data is used to evaluate the forecast accuracy of NASA’s Global GEOS Composition Forecasting (GEOS-CF) model. GEOS-CF makes daily three-dimensional forecasts of trace gases and aerosol species. Overall, for calm periods, the forecast model predicts lower tropospheric ozone at NASA Langley with reasonable accuracy (within 20%). The model best predicts the timing and extent of stratospheric intrusions but often vary in the magnitude of the ozone mixing ratio. Among the other types of atmospheric conditions, there is more variability in the model forecasts. Based on this analysis, model forecasts are utilized to determine future data acquisition opportunities with the goal of providing feedback to the modeling teams, thereby enabling them to better understand the model biases and improve the model forecasts of ozone during these different atmospheric conditions.

Daniel B Phoenix↗
NASA NTRS1992

Measured and calculated optical property profiles in the mixed layer and free troposphere

Nearly simultaneous measurements of the physical and optical properties of mixed layer and free tropospheric aerosols near Boulder, Colorado, were made on several occasions using aircraft, balloon, and ground-based sensors. This effort (Front Range Lidar, Aircraft, and Balloon experiment (FRLAB)) was conducted with the purpose of obtaining a diverse, self-consistent data set that could be used for testing optical model calculations based on measured physical characteristics such as apparent size distribution, composition, and shape. It was found that even with the uncertainties involved, the model predictions are in good agreement with the measurements in the visible and near infrared wavelength regions. At CO2 lidar wavelengths there is considerably more uncertainty in both the calculated and measured values; however, within the estimated errors there appears to be satisfactory agreement except for the highest free tropospheric layer studied. The results also indicate that during FRLAB the aerosol in the boundary layer and free troposphere behaved as spherical particles for optical modeling purposes. The utility of the observations for determining the extinction-to-backscatter ratio relevant to aerosols in the boundary layer and free troposphere is described with typical measured values being in the 20 to 30 sr range.

Rosen, James M.↗
NASA NTRS2023

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM 2.5 ). Ground-level PM 2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM 2.5 estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM 2.5 over four Indian sub-regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM 2.5 estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM 2.5 as a first step toward estimating particle composition. Our comparison of AutoML-derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user-chosen models. The idealized pseudo-observations (chemical-transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.

Machine learning↗
DOE OSTI2025

Interactive Gas Chemistry for Enhanced Science Capabilities of the Energy Exascale Earth System Model Version 3

Atmospheric chemistry plays a crucial role in Earth system models (ESMs), controlling atmospheric composition and radiative balance; it is highly interactive with the physical climate, biogeochemical cycles, and human systems. However, it often imposes computational challenges in an ESM. Here we develop a full troposphere‐stratosphere interactive chemistry module for the US Department of Energy's Energy Exascale Earth System Model (E3SM). We intentionally build a streamlined module based on E3SM version 2 that interacts with other components and maintains all of major chemical and chemistry‐climate feedbacks. The module incorporates a new, highly efficient tracer advection scheme; linearization of stratospheric chemistry; and abridged tropospheric chemical mechanism with 28 reactive tracers. This new model, E3SM‐chem, can readily perform century‐long climate simulations of ozone, methane, and nitrous oxide based on emission scenarios as well as provide hourly budgets for the gas‐phase radicals that drive aerosol chemistry. We evaluate E3SM‐chem with an atmosphere‐only simulation as in the recent climate model intercomparison project (CMIP6) finding results similar to the other CMIP6 models. For the present‐day, E3SM‐chem matches the standard measurement metrics for stratospheric and tropospheric ozone, surface air quality, other key reactive gases like carbon monoxide, and the methane lifetime. Overall, E3SM‐chem maintains the climate fidelity of the baseline model while adding at most 20% to the computational cost of the atmosphere model. Hence, interactive chemistry can be a default configuration for long climate simulations at resolutions of 1° or finer, which is crucial for producing self‐consistent chemistry‐climate feedbacks that alter the climate system.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Aerosol Retrievals Using Channel 1 and 2 AVHRR Data

The effect of tropospheric aerosols on global climate via the direct and indirect radiative forcings is one of the largest remaining uncertainties in climate change studies. Current assessments of the direct aerosol radiative effect mainly focus on sulfate aerosols. It has become clear, however, that other aerosol types like soil dust and smoke from biomass burning are also likely to be important climate forcing factors. The magnitude and even the sign of the climate forcing caused by these aerosol types is still unknown. General circulation models (GCMs) can be used to estimate the climatic effect of the direct radiative forcing by tropospheric and stratospheric aerosols. Aerosol optical properties are already parameterized in the Goddard Institute for Space Studies GCM. Once the global distribution of aerosol properties (optical thickness, size distribution, and chemical composition) is available, the calculation of the direct aerosol forcing is rather straighfforward. However, estimates of the indirect aerosol effect require additional knowledge of the physics and chemistry of aerosol-cloud interactions which are still poorly understood. One of the main objectives of the Global Aerosol Climatology Project, established in 1998 as a joint initiative of NASA's Radiation Science Program and GEWEX, is to infer the global distribution of aerosols, their properties, and their seasonal and interannual variations for the full period of available satellite data. This will be accomplished primarily through a systematic application of multichannel aerosol retrieval algorithms to existing satellite data and advanced 3-dimensional aerosol chemistry/transport models. In this paper we outline the methodology of analyzing channel 1 and 2 AVHRR radiance data over the oceans and describe preliminary retrieval results.

Mishchenko, Michael I.↗
NASA NTRSDate not supplied

Quantifying the benefits of improved satellite remote-sensing observations for inverse modeling of NOx and NMVOC emissions

This study aims to demonstrate the benefits of using novel high spatiotemporal retrieval products from newer satellites for top-down emission estimates of nitrogen oxides (NO x ) and non-methane volatile organic compounds (NMVOCs) for the summer of 2019 over the contiguous United States. Recent satellite retrievals have not only advanced spatiotemporal resolution but also greatly reduced error and uncertainty due to reduced noise in the retrievals compared to spaceborne sensors launched in the past. We applied inverse modeling techniques using tropospheric nitrogen dioxide (NO 2 ) and formaldehyde (H-CHO) column retrieval products from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) in conjunction with the Weather Research Forecast and Community Multiscale Air Quality Modeling system (WRF-CMAQ). In order to provide a better representation of background chemical composition and avoid misalignment of emission adjustment, we applied monthly scaling factors for ozone (O 3 ) and CO boundary concentrations in addition to the inclusion of lightning and aviation emissions. Satellite-constrained NO x and NMVOCs posterior emissions showed a mitigated discrepancy between observed and modeled columns. The improvement in the model performance was greater when using TROPOMI, primarily benefiting from reduced errors/biases of the satellite retrievals that enabled us to explore corresponding changes in O 3 concentrations and production sensitivity regimes using the ratio of H-CHO and NO 2 .

remote sensing↗
NASA NTRSDate not supplied

Model Evaluation and Intercomparison Using Data Collected by the Langley Mobile Ozone Lidar in Hampton, Virginia

Throughout the year 2022, the Langley Mobile Ozone Lidar (LMOL) has been frequently collecting ozone and aerosol measurements in the lower troposphere. The lidar is located in the parking lot behind the Atmospheric Sciences building at NASA Langley in Hampton, Virginia, approximately 37.095 N, -76.389 W. The data was collected during a range of several distinct atmospheric conditions, including stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. We use this data to evaluate and intercompare the forecast accuracy of two atmospheric chemistry models: the GEOS Composition Forecasting model (GEOS-CF) and the Weather Research and Forecasting model with Chemistry (WRF-Chem). Both models make daily three-dimensional forecasts of many trace gases and species for the contiguous United States. Over a range of ten distinct collection events, each spanning 36 hours to nearly seven days, the forecast models predict tropospheric ozone at NASA Langley with reasonable accuracy. The models best predict the timing and shape of the observed stratospheric intrusions in the middle troposphere but often vary in the magnitude of the ozone mixing ratios. For other atmospheric conditions, there is more variability in the model accuracy. Here, we summarize the accuracy of the models for all events and investigate reasons for differences between the models and the lidar.

Daniel B. Phoenix↗
NASA NTRS1994

Global Ozone and Reactive Nitrogen : Composition, Chemistry and Sources

Ozone plays a central role in the chemistry of the atmosphere both as an ultraviolet shield and as a source of hydroxyl radicals (OH), a potent initiator of atmospheric chemistry. There is evidence to suggest that the ozone abundance in the troposphere (0-10 km) has doubled since the industrial revolution and continues to increase to date. The principle reason for this increase is thought to be the increasing emissions of nitrogen oxides (NO(x)) from anthropogenic activities. Although NO(x) is highly reactive and its products such as HN03 are easily removed by deposition, it now appears that its chemistry is quite complex and it can be transported over long distances via its conversion to a variety of nitrates and penetrates. The sources of atmospheric NO(x) include free tropospheric sources such as lightning and subsonic aircraft, as well as surface emissions which are transported to the free troposphere via convective processes. Recent experimental and theoretical studies have tried to unravel the chemistry of reactive nitrogen species, its sources, and their role in ozone formation. In this presentation we shall describe the results from these studies.

Sing, Hanwant B.↗
NASA NTRS1998

Characterization of Mesoscale Convective Systems by Means of Composite Radar Reflectivity Data

A mesoscale convective system (MCS) is broadly defined as a cloud and precipitation system of mesoscale dimensions (often too large for most aircraft to circumnavigate) with deep-convective activity concentrated in at least part of the MCS, or present during part of its evolution. A large areal fraction of MCSs is stratiform in nature, yet estimates from MCSs over the Great Plains, the Southeast, and tropical waters indicate that at least half of the precipitation is of convective origin. The presence of localized convection is important, because within convective towers cloud particles and hydrometeors are carried upward towards the cloud top. Ice crystals then move over more stratiform regions, either laterally, or through in situ settling over decaying and spreading convection. These ice crystals then grow to precipitation-size particles in mid- to upper tropospheric mesoscale updrafts. The convective portion of a MCS is often a more or less continuous line of thunderstorms, and may be either short-lived or long-lived. Geerts (1997) presents a preliminary climatology of MCSs in the southeastern USA, using just one year of composite digital radar reflectivity data. In this study MCSs are identified and characterized by means of visual inspection of animated images. A total of 398 MCSs were identified. In the warm season MCSs were found to be about twice as frequent as in the cold season. The average lifetime and maximum length of MCSs are 9 hours, and 350 km, respectively, but some MCSs are much larger and more persistent. In the summer months small and short-lived MCSs are relatively more common, whereas in winter larger and longer-lived systems occur more frequently. MCSs occur more commonly in the afternoon, in phase with thunderstorm activity, but the amplitude of the diurnal cycle is small compared to that of observed thunderstorms. It is estimated that in the Southeast more than half of all precipitation and severe weather results from MCSs.

Geerts, Bart↗
NASA NTRS2010

Comparison of GOES-Retrieved and In Situ Measurements of Deep Convective Anvil Cloud Microphysical Properties During the Tropical Composition, Cloud and Climate Coupling Experiment (TC(sup 4))

One of the main goals of the Tropical Composition, Cloud and Climate Coupling Experiment (TC(sup 4)) during July and August 2007 was to gain a better understanding of the formation and life cycle of cirrus clouds in the upper troposphere and lower stratosphere and how their presence affects the exchange of water vapor between these layers. Additionally, it is important to compare in situ measurements taken by aircraft instruments with products derived from satellite observations and find a meaningful way to interpret the results. In this study, cloud properties derived using radiance measurements from the Geostationary Operational Environmental Satellite (GOES) imagers are compared to similar quantities from aircraft in situ observations and are examined for meaningful relationships. A new method using dual \angle satellite measurements is used to derive the ice water content (IWC) for the top portion of deep convective clouds and anvils. The results show the in situ and remotely sensed mean microphysical properties agree to within approx.10 microns in the top few kilometers of thick anvils despite the vastly different temporal and spatial resolutions of the aircraft and satellite instruments. Mean particle size and IWC are shown to increase with decreasing altitude in the top few kilometers of the cloud. Given these relationships, it may be possible to derive parameterizations for effective particle size and IWC as a function of altitude from satellite observations

Yost, Christoher R.↗
NASA NTRSDate not supplied

The SPARC-Reanalysis Intercomparison Project: Summary of Phase 1 and Plans for Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Jonathan S Wright↗