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Operations Mission Planner for the 26 Meter (26M) Antenna Subnetwork (OMP-26) is an interactive scheduling tool developed by the Jet Propulsion Laboratory to assist in allocating the Deep Space Network (DSN) 26M antennas to support Earth-orbiting spacecraft (SC).
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In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.
The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.
The future trajectory of the stratospheric ozone recovery will be sensitive to greenhouse gas concentrations through thermal control of chemical loss and via stratospheric circulation changes. The latter in particular is subject to considerable uncertainty meriting continuing monitoring of the evolution of ozone throughout the depth of the stratosphere. Atmospheric reanalyses utilize the data assimilation methodology to obtain comprehensive representations of the state of the atmosphere, including its composition, on multidecadal scales by combining diverse measurements from satellite-borne and conventional data sources. Systematic biases among these various data types pose a challenge for assimilation by introducing spurious discontinuities that affect the utility of reanalyses for studies of long-term variability and trends.In this presentation we will outline an approach, developed at NASA's Global Modeling and Assimilation Office (GMAO), that allows joint assimilation of stratospheric ozone profiles from the Microwave Limb Sounder (MLS) on EOS Aura and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) currently flying on the Suomi-NPP satellite with future missions projected into the 2030s. We will demonstrate that a simple offline correction significantly reduces biases between MLS and OMPS-LP ozone data providing a strategy for generating a long-term vertically resolved homogenized representation of stratospheric ozone in future reanalyses. One novel element of our approach compared to previous GMAO reanalysis is the use of a version of the Goddard Earth Observing System model with full stratospheric chemistry. We will show selected comparisons of MLS and OMPS-LP assimilation experiments with independent ozonesonde and satellite data as well as two examples of process-based evaluation focused on the 2016 QBO disruption and Arctic winter ozone loss focusing on the relative performance of the MLS and OMPS-LP analyses.
Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statistically-sound uncertainty quantification, and are trained on both scattering and bound-state data from a wide area of the nuclear chart. Combined together, these features allow for significant improvement in fidelity and extrapolative power of the model. Here, we present preliminary work toward the development and training of such an OMP. The capability of the model to describe data at this first stage is encouraging.
The alpha optical model potential (𝛼-OMP ) is a phenomenological approach used to describe elastic scattering where multiple reaction channels are open. It is one of the most critical inputs for the calculation of thermonuclear reaction rates in explosive stellar environments, but uncertainties within the 𝛼-OMP lead to imprecise predictions hindering comparisons between calculations and observations. In order to improve the precision of the 𝛼-OMP, additional nuclear physics data are required. In this paper, a measurement of the 86 Sr (𝛼, 𝛼) elastic scattering cross section at multiple energies is reported. Here, a local optical potential is constructed via a fully Bayesian analysis of the elastic scattering data. The resulting uncertainties on the low-energy cross sections relevant to nuclear astrophysics are then calculated and shown to be on the order of 50%.
The findings are documented of the OMP research task, which investigated the applicability of artificial intelligence (AI) technology in support of automated scheduling. The goals of the effort are summarized and the technical accomplishments are highlighted. The OMP task succeeded in identifying how AI technology could be applied and demonstrated an AI-based automated scheduling approach through the OMP prototypes.
Explored here is the user interface problems encountered with the Operations Missions Planner (OMP) project at the Jet Propulsion Laboratory (JPL). OMP uses a unique iterative approach to planning that places additional requirements on the user interface, particularly to support system development and maintenance. These requirements are necessary to support the concepts of heuristically controlled search, in-progress assessment, and iterative refinement of the schedule. The techniques used to address the OMP interface needs are given.
Monthly mean August 2014 Version-6.28 AIRS and CrIS products agree well with OMPS and CERES, and reasonably well with each other. Version-6.28 CrIS total precipitable water is biased dry compared to AIRS. AIRS and CrIS Version-6.36 water vapor products are both improved compared to Version-6.28. Version-6.36 AIRS and CrIS total precipitable water also shows improved agreement with each other. AIRS Version-6.36 total ozone agrees even better with OMPS than does AIRS Version-6.28, and gives reasonable results during polar winter where OMPS does not generate products. CrIS and ATMS are high spectral resolution IR and Microwave atmospheric sounders currently flying on the SNPP satellite, and are also scheduled for flight on future NPOESS satellites. CrIS/ATMS have similar sounding capabilities to those of the AIRS/AMSU sounder suite flying on EOS Aqua. The objective of this research is to develop and implement scientifically equivalent AIRS/AMSU and CrIS/ATMS retrieval algorithms with the goal of generating a continuous data record of AIRS/AMSU and CrIS/ATMS level-3 data products with a seamless transition between them in time. To achieve this, monthly mean AIRS/AMSU and CrIS/ATMS retrieved products, and more importantly their interannual differences, should show excellent agreement with each other. The currently operational AIRS Science Team Version-6 retrieval algorithm has generated 14 years of level-3 data products. A scientifically improved AIRS Version-7 retrieval algorithm is expected to become operational in 2017. We see significant improvements in water vapor and ozone in Version-7 retrieval methodology compared to Version-6.We are working toward finalization and implementation of scientifically equivalent AIRS/AMSU and CrIS/ATMS Version-7 retrieval algorithms to be used for the eventual processing of all AIRS/AMSU and CrIS/ATMS data. The latest version of our retrieval algorithm is Verison-6.36, which includes almost all the improvements we want in Version-7. Version-6.28 has been used to process both AIRS and CrIS data for August 2014. This poster compares August 2014 monthly mean Version-6.28 AIRS/AMSU and CrIS/ATMS products with each other, and also with monthly mean products obtained using AIRS Version-6. AIRS and CrIS results using Version-6.36 are presented for April 15, 2016. These demonstrate further improvements since Version-6.28. The new results also show improved agreement of Version-6.36 AIRS and CrIS products with each other. Version-6.36 is not yet optimized for CrIS ozone products.
The TROPspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (Sentinel-5P) is the first of the Atmospheric Composition Sentinels by the European Space Agency (ESA) that provides measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The early afternoon orbit of Sentinel-5P mission provides a strong synergy with the U.S. Suomi National Polar-orbiting Partnership (S-NPP) satellite, especially in that the S-NPP Ozone Monitoring and Profiling Suite (OMPS) facilitates high vertically resolved stratospheric and lower mesospheric ozone profiles. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) supports over a thousand data collections in the Focus Areas of Atmospheric Composition, Water & Energy Cycles, and Climate Variability and it is the Distributed Active Archive Center (DAAC) that is curating both offline Sentinel-5P TROPOMI and S-NPP OMPS Level-1B (L1B) and Level-2 (L2) products. Through its convenient and enhanced tools/services such as OPeNDAP and L2 Subsetting, GES DISC offers air quality remote sensing user communities facile solutions for complex Earth science data and applications. This presentation will demonstrate TROPOMI and OMPS products including earthview radiance, solar irradiance, and currently available L2 datasets, as well as easy ways to access, visualize and subset data. The implementation of the End User License Agreement (EULA) between NASA GES DISC and all data users accessing data at GES DISC will be emphasized as well.
Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles, and the long-range transport of the smoke plumes poses great impact to climate change, such as altering the Antarctic ozone (O3) hole. Spaceborne observations from satellites have been proven successful in monitoring CO from wildfires in the past decade and can be used to examine the associated CO transport. The primary remote sensing data used in this study is the Single Field of View (SFOV) Sounder Atmospheric Product (SiFSAP) derived from the Cross-track Infrared Sounder (CrIS) on SNPP, a new product that will be generated at NASA and released to the public in a few months. SiFSAP has a high spatial resolution of ~14 km and includes atmospheric profiles of temperature, water vapor, CO, O3, as well as cloud products, and as the SiFSAP algorithm performs both day- and night-time retrievals, it provides an additional observation for night-time in comparison to the day-time-only solar polar satellite instruments such as MOPPIT, OMPS, and TROPOMI. Most recent significant improvements in the quality of this product plus its good spatial resolution (better than most global weather and climate models) enable us to conduct process-oriented analysis of CO transport, and the variation of water vapor, O3 and clouds (optical depth, effective radius and cloud height) within and around the fire plumes. The wind fields from MERRA-2 reanalysis will be used to help characterize the transport. Other remote sensing data, such as CO from AIRS, MOPPIT and TROPOMI, O3 from OMPS and TROPOMI, as well as the UV aerosol index from OMI and OMPS, will be used and compared with SiFSAP sounder products. Cross-validation of CO and O3 from these different sensors with the corresponding MERRA-2 products will also be carried out. Along the plume transport path, some enhancement of O3 and correlation between fire aerosol, cloud optical depth and water vapor have been found but need further investigation. Using the transport of fire plumes over the Southern Pacific Ocean as a unique testbed, this study will demonstrate the trace species monitoring capability of current satellite observations, in particular, for CO and O3, and hopefully, it will provide some insight on the impacts on fire emissions on air quality and climate.
Sulfur dioxide (SO 2 ) measurements from the Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite (OMPS), and TROPOspheric Monitoring Instrument (TROPOMI) satellite spectrometers were used to update and extend the previously developed global catalogue of large SO 2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources releasing from about 10 kt yr −1 to more than 4000 kt yr −1 of SO 2 , that have been identified and grouped by country and primary source origin: volcanoes (106 sources); power plants (477); smelters (74); and sources related to the oil and gas industry (102). There are several major improvements compared to the original catalogue: it combines emissions estimates from three satellite instruments instead of just OMI, uses a new version 2 of the OMI and OMPS SO 2 dataset, and updated consistent site-specific air mass factors (AMFs) are used to calculate SO 2 vertical column densities (VCDs). The newest TROPOMI SO 2 data processed with the Covariance-Based Retrieval Algorithm (COBRA), used in the catalogue, can detect sources with emissions as low as 8 kt yr −1 (in 2018–2021) compared to the 30 kt yr −1 limit for OMI. In general, there is an overall agreement within ±12 % in total emissions estimated from the three satellite instruments for large regions. For individual emission sources, the spread is larger: the annual emissions estimated from OMI and TROPOMI agree within ±13 % in 50 % of cases and within ±28 % in 90 % of cases. The version 2 catalogue emissions were calculated as a weighted average of emission estimates from the three satellite instruments using an inverse-variance weighting method. OMI, OMPS, and TROPOMI data contribute 7 %, 5 %, and 88 % to the average, respectively, for small (<30 kt yr −1 ) sources and 33 %, 20 %, and 47 %, respectively, for large (>300 kt yr −1 ) sources. The catalogue data show an approximate 50 % decline in global SO 2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years. The version 2 of the global catalogue has been posted at the NASA global SO 2 monitoring website (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022).
Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statisticallysound uncertainty quantification, and are trained on both scattering and bound-state data from a wide area of the nuclear chart. Combined together, these features allow for significant improvement in fidelity and extrapolative power of the model. Here, we present preliminary work toward the development and training of such an OMP. The capability of the model to describe data at this first stage is encouraging.