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The Stratospheric Aerosol and Gas Experiment (SAGE) III is an external payload on the International Space Station (ISS) launched on SpaceX CRS-10 on 19 February 2017 and robotically installed on ExPRESS Logistics Carrier-4 (ELC-4). First light was acquired on 17 March 2017.
During the 2019/2020 fire season, Australian bushfires burned 46 million acres, killed 39 people and billions of animals, and became the costlier fire season in Australian history. Between the end of December and early January, a series of pyrocumulonimbus injected thick smoke layers in the upper troposphere and lower stratosphere which were observed over the Tasmanian Sea and New Zealand by the Ozone Mapping and Profiler Suite (OMPS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The smoke plume crossed the tropopause and was further dispersed across the Eastern Pacific Ocean rising up to 30 km height after two weeks. . In this study, we use CALIPSO and the NASA Langley Trajectory Model (LaTM), driven by Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2) meteorological data, to track the dispersion of Australian fire smoke plumes in both the troposphere and the stratosphere. Trajectory mapping is used to re-construct the 3-dimension structure of the smoke plumes. Results are compared with independent observations from the Stratospheric Aerosol and Gas Experiment (SAGE) III on the International Space Station (ISS) to understand the complex transport of the plume into the stratosphere and its lifetime. Using trajectory maps, the impact of the Australian bushfires on the Earth’s radiative energy budget is assessed with the Clouds and the Earth’s Radiant Energy System (CERES).
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.
The 2019-2020 season has been one of the worst fire seasons on record. Australia has seen unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. NASA's satellites not only tracked the event in real time, using resources such as the Global Actives Fires and Hotspots Dashboard you see below, but also collected large volumes of rich data that scientists and researchers can use to study the event and the regional and global effects of the disaster. In this Esri StoryMap, we will guide you through the factors leading up to the 2019-2020 Australian bushfires disaster, the effect this event has had on air quality and global atmospheric composition, and the science behind researching the tie between disasters and public health. This story map will use data from ASDC-supported NASA missions such as the Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), Stratospheric Aerosol and Gas Experiment (SAGE III) on the International Space Station (ISS), and Multi-angle Imaging SpectroRadiometer (MISR).
Stratospheric intrusions are the transport of ozone from the stratosphere to the troposphere. These intrusions are frequent in spring; they can affect local air quality as well as satellites observations, notably limb sounding instruments such as SAGE III. Complex models such as GEOS-CF are used to assess the magnitude and the impact of SI. We present the model-data comparison of several stratospheric intrusion above Hampton, Virginia, in 2019, using the NASA Langley Mobile Ozone Lidar and the GEOS-CF model. We show the limits of the model-data agreement for these events and suggest a method to correct limb-sounding observations.
Ozone observed by seven satellite instruments, the Atmospheric Trace Molecule Spectroscopy instrument (ATMOS), Stratospheric Aerosol and Gas Experiment (SAGE) II, Polar Ozone and Aerosol Measurement (POAM) II instrument, Halogen Occultation Experiment (HALOE), Microwave Limb Sounder (MLS), Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA), and Millimeter-wave Atmospheric Sounder (MAS), during early November 1994 is mapped in equivalent latitude/potential temperature (EqL/ theta )space to facilitate nearly global comparisons of measurements taken in similar air masses.
An intercomparison of ozone profiling instruments, two differential absorption lidars, a microwave radiometer, electrochemical concentration sondes, and the SAGE II satellite instrument is presented.
The OMPS Limb Profiler (LP) instrument is de-signed to provide high-vertical-resolution ozone and aerosol profiles from measurements of the scattered solar radiation in the 290–1000 nm spectral range. It collected its first Earth limb measurement on 10 January 2012 and continues to provide daily global measurements of ozone and aerosol profiles from the cloud top up to 60 and 40 km, respectively. The relatively high vertical and spatial sampling allow detection and tracking of sporadic events when aerosol particles are injected into the stratosphere, such as volcanic eruptions or pyro cumulonimbus (PyroCb) events. In this paper we discuss the newly released Version 2.0 OMPS multi-wavelength aerosol extinction coefficient retrieval algorithm. The algorithm now produces aerosol extinction profiles at 510, 600, 674, 745, 869 and 997 nm wavelengths. The OMPS LP Version 2.0 data products are compared to the SAGE III/ISS, OSIRIS and CALIPSO missions and shown to be of good quality and suitable for scientific studies. The comparison shows significant improvements in the OMPS LP retrieval performance in the Southern Hemisphere (SH) and at lower altitudes. These improvements arise from use of the longer wavelengths, in contrast with the V1.0 and V1.5 OMPS aerosol retrieval algorithms, which used radiances only at 675 nm and therefore had limited sensitivity in those regions. In particular, the extinction coefficients at 745, 869 and 997 nm are shown to be the most accurate, with relative accuracies and precisions close to 10% and 15%, respectively, while the 675 nm relative accuracy and precision are on the order of 20%. The 510 nm extinction coefficient is shown to have limited accuracy in the SH and is only recommended for use between 20–24 km and only in the Northern Hemisphere. The V2.0 retrieval algorithm has been applied to the complete set of OMPS LP measurements, and the new dataset is publicly available.
In this study, we evaluate a decadal ozone profile record derived from Suomi NPP Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP). The data from OMPS LP were re-processed with the new retrieval algorithm from 2012 to present. The new reprocessing includes adjustments in both Level 1 and Level 2 algorithms. The sensor’s altitude registration, updates in radiometric calibrations, under-sampling and stray light corrections were addressed in Level 1 processing. The new retrieval algorithm (version 2.7) combines measurements from UV and VIS parts of the spectra to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km, unlike to previous version 2.5 where ozone profiles were retrieved independently from UV and VIS. To improve ozone retrievals in the Upper Troposphere Lower Stratosphere (UTLS) region, we implemented a new aerosol correction that assumes a gamma-function particle size distribution. In addition, ozone and nitrogen dioxide cross sections and climatologies were updated. We discuss sensitivity of the ozone retrievals to implemented changes in Level 1 and Level 2 algorithms. We focus our analysis on evaluation of the stability of the 10-year record from OMPS LP. We show results from the internal methods for characterizing the stability of the LP altitude registration and compare the LP reflectivity and ozone with the similar quantities measured by OMPS nadir sensors. Finally, we will show results from comparisons with independent satellite (MLS and SAGE III) and ozone sonde measurements.
In this presentation we evaluate ozone profile retrievals from Ozone Mapping and Profiling Suite Limb Profiler (OMPS-LP) launched in October 2011 on Suomi NPP satellite. The data from OMPS-LP were re-processed with the new retrieval algorithm that combines measurements from UV and VIS parts of the spectra to retrieve a single ozone profile between 12.5 km (or cloud tops) and 57.5 km. To improve ozone retrievals in the Upper Troposphere Lower Stratosphere (UTLS) region, a new aerosol correction with a gamma-function particle size distribution was implemented. The OMPS-LP altitude registration was also adjusted in the new version to ensure the stability of the ozone record. The continuous ozone profile observations with Suomi NPP OMPS LP exceeds 10 years. We will compare OMPS-LP ozone profiles with coincident stratospheric ozone measurements from NASA Aura MLS, ISS SAGE III, and OMPS NP and with the observations in UTLS and middle stratosphere from a ground-based network of ozone sonde stations to evaluate accuracy and precision of OMPS-LP retrievals and overall stability of the record. We will also investigate the quality of stratospheric ozone columns obtained from OMPS-LP and estimate the contribution of various errors to integrated stratospheric columns from OMPS-LP.
The most extreme manifestation of a fire–weather interaction is the formation of pyrocumulonimbus (pyroCb) thunderstorms, triggered by super-heated updrafts, which can eject smoke at altitudes exceeding 20 Km. In this study, we investigated climate-related impacts from the most intensive pyroCb-triggered injection of smoke in the stratosphere: the Australian New Year wildfires in 2019/2020. We first provide a general overview of the vision and objectives of the StratoFIRE project. With the aid of the global chemistry-climate model EMAC, we then simulate radiative and chemical perturbations in the stratosphere in relation to 0.9 Tg smoke in the stratosphere assuming different injection heights, from 13 to 16 Km. The simulation of stratospheric optical depth perturbations were found to be sensitive to the assumed injection height, with a maximum height at 16 Km showing the best agreement with the GLOSSAC and SAGE-ISS aerosol extinction observations.
Using the NASA GEOS Chemistry Climate Model (CCM) coupled to a detailed sectional aerosol microphysical model, we have simulated the January 2022 Hunga Tonga-Hunga Ha’apai (HTHH) plume. We have developed a version of the Global Modeling Initiative (GMI) chemistry mechanism to include the requisite sulfur chemistry and coupled it to the Community Aerosol and Radiation Model for Atmospheres (CARMA) sectional aerosol microphysics within the GEOS CCM. Our simulations focus on the lifecycle of stratospheric sulfur injected by the eruption–from the oxidation of the injected sulfur dioxide to the nucleation, growth, coagulation, hydration, and eventual settling of the volcanic sulfate aerosol particles. In order to constrain the simulated microphysics, we focus on comparing our model to observations with aerosol size information, namely in-situ particle counter observations and multi-wavelength satellite observations from OMPS-LP and SAGE-III. This eruption provides an interesting test case for our model; both the modeled gas-phase chemistry as well as the modeled aerosol microphysics are perturbed by the massive injection of water that accompanied the primary sulfur emissions from the eruption.
The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.
The way volcanic clouds evolve is very sensitive to the initial spatial 3D distributions of volcanic materials, which are often unknown. In this study, we conducted inverse modeling of the Mt. Pinatubo cloud using TOMS 2D mapping of Aerosol Index and SO 2 loading during the first three post-eruption days to estimate the time-dependent emissions profiles and initial 3D spatial distributions of volcanic ash and SO 2 . We account for aerosol radiative feedback and dynamic lofting of volcanic ash in the inversion calculations for the first time. This resulted in a lower ash injection height (by 1.5 km for ash) than without ash radiative feedback. The Pinatubo eruption ejected ≈77% of fine ash at 12 to 23 km, ≈65% of SO 2 at 18–25 km. In contrast with previous studies, which suggested that all volcanic materials were emitted above the tropopause, a significant fraction of SO 2 (5.1 of 15.5 Mt) and fine ash (37.2 of 66.5 Mt) were ejected in the troposphere, where SO 2 quickly oxidized into sulfate aerosol that is short-lived in the troposphere. This explains the early presence of sulfate aerosols in the plume and why the models can reproduce the observed volcanic aerosols' optical depth (AOD), assuming lower-than-observed SO 2 emission in the stratosphere. Despite the quicker than in observations build-up of sulfate AOD, in a month after the eruption, the evolution of the Pinatubo AOD simulated using the obtained ash and SO 2 initial distributions converges with the available stratospheric aerosol and gas experiment (SAGE) observations.
Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emission limits and the wide range of chemical reactivity in pipeline quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its methane number (analogous to the octane number for liquid SI fuels) can span from 65 - 95. In order to realize diesel-like efficiencies, SI natural gas engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding the combustion-engine interactions pertaining to flame propagation and end-gas autoignition (EGAI). However, EGAI, if controlled, provides an opportunity to increase SI natural gas engine efficiency by increasing combustion rate and the total burned fuel, mitigating the effects of the slow flame speeds of natural gas fuels which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, in order to study EGAI phenomenon, the present work highlights multi-dimensional computational fluid dynamics (CFD) models of the Cooperative Fuel Research (CFR) engine. The CFR engine models are used to investigate fuel-engine interactions that lead to EGAI with natural gas, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). A Three-Pressure Analysis, performed with GT-Power, was used to estimate initial and boundary conditions for the three-dimensional CFD model. CONVERGE CFD v2.4 was used for the three-dimensional CFD modeling where the level set G-Equation model and SAGE detailed chemical kinetics solver were used. An assessment of the different modeling approaches is also provided to evaluate their limitations, advantages and disadvantages, and for which situations they are most applicable. Model validation was performed with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction, and IMEP and shows good agreement in Peak Cylinder Pressure (PCP), PCP crank angle, and the location of the 10%, 50%, and 90% mass fraction burned (CA10, CA50, and CA90, respectively). The models can predict the onset crank angle and pressure rise rate for light, medium, and heavy EGAI under a variety of fuel reactivities and engine operating conditions.
We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.
Neutrinos are among the most abundant fundamental particles in the universe, and yet they are so elusive that they pass through almost everything without leaving a trace. For decades, researchers have measured neutrino oscillations in great detail, refining our understanding of the theories. Yet, once again, neutrino shows their incredibly surprising nature through some recent experimental results, suggesting that our current understanding might be incomplete. Several experiments have observed an unexpected difference in the observed neutrino behaviour from what the Standard Model predicts, showing an excess in some cases (e.g. LSND, MiniBooNE) and a deficit of neutrino events in others (e.g. SAGE, GALLEX). These anomalies challenge our conventional theories of standard three neutrino flavors and phenomenologically motivate the sterile neutrino hypothesis, which does not interact through the usual weak force. While some results appear consistent with this idea, others have found no supporting evidence (e.g. MicroBooNE), leaving the question unanswered. NOvA is one such long-baseline neutrino experiment with its Near Detector (ND) at Fermilab and a Far Detector (FD) at 810 km in Minnesota. It uses the NuMI beam as its primary source of neutrinos for oscillation studies. Its two-detector setup, extended baseline, and ability to conduct combined $\nu$-$\bar\nu$ analyses position it to explore these phenomena in new ways. We use the joint $\nu_{\mu}$ and neutral current (NC) disappearance channels to probe active-sterile mixing in the 3+1 model. This analysis utilizes a data sample of 27$\times$10$^{20}$ protons on target (POT) in neutrino mode and 12.5$\times$10$^{20}$ protons on target (POT) in antineutrino mode. We found no evidence of sterile neutrino at 90$\%$ C.L. We present the limits in $\sin^{2}\theta_{24}$ vs $\Delta m^{2}_{41}$, $\sin^{2}\theta_{34}$ vs $\Delta m^{2}_{41}$, and $\sin^{2}2\theta_{\mu\tau}$ vs $\Delta m^{2}_{41}$ parameter spaces. The limits provided by this analysis are world leading in most of the parameter spaces.
Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.