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At least 163 records · Page 9

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.

58 GEOSCIENCES↗

Planning, Implementation, and Scientific Goals of the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) Field Missions

The Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission based at Ellington Field, Texas, during August and September 2013 employed the most comprehensive airborne payload to date to investigate atmospheric composition over North America. The NASA ER-2, DC-8, and SPEC Inc. Learjet flew 57 science flights from the surface to 20 km. The ER-2 employed seven remote sensing instruments as a satellite surrogate and eight in situ instruments. The DC-8 employed 23 in situ and five remote sensing instruments for radiation, chemistry, and microphysics. The Learjet used 11 instruments to explore cloud microphysics. SEAC4RS launched numerous balloons, augmented Aerosol RObotic NETwork, and collaborated with many existing ground measurement sites. Flights investigating convection included close coordination of all three aircraft. Coordinated DC-8 and ER-2 flights investigated the optical properties of aerosols, the influence of aerosols on clouds, and the performance of new instruments for satellite measurements of clouds and aerosols. ER-2 sorties sampled stratospheric injections of water vapor and other chemicals by local and distant convection. DC-8 flights studied seasonally evolving chemistry in the Southeastern U.S., atmospheric chemistry with lower emissions of NOx and SO2 than in previous decades, isoprene chemistry under high and low NOx conditions at different locations, organic aerosols, air pollution near Houston and in petroleum fields, smoke from wildfires in western forests and from agricultural fires in the Mississippi Valley, and the ways in which the chemistry in the boundary layer and the upper troposphere were influenced by vertical transport in convective clouds.

Toon, Owen B.↗

Organisation of Diverse Mechanisms of Secondary Ice Production among Basic Convective and Stratiform Cloud-types

This 3-year DoE-funded joint project had the over-arching aim of understanding how ice is initiated in clouds of various types. Focus was given to processes of fragmentation of pre-existing ice, which can occur in positive feedback loops (‘ice multiplication’). A basic question to address was which fragmentation processes prevail in which basic cloud-types. The approach was to use cloud models and field observations, while pioneering our own lab observations of ice initiation to break the deadlock from the past lack of lab observations. Historically, the tendency of the cloud physics community to avoid doing lab observations has allowed a vast gap in knowledge about ice initiation to persist for decades. During the first part of the project, new formulations were created to treat two overlooked types of fragmentation of ice. First, sublimational breakup of ice was treated based on a theoretical formula that we fitted to a pooled dataset of lab observations published previously in the literature. Second, a new mode of fragmentation of freezing raindrops was treated, which involves a supercooled drop being hit by a more massive ice particle. Some of the secondary droplets from the impact freeze. This work was done at Manchester University by Co-I Connolly. Then during the second part, both formulations were implemented in our ‘aerosol-cloud model’ (AC). AC has a hybrid bin/bulk microphysics scheme, and now represents four processes of SIP. The accuracy of AC was evaluated for four cases typifying four basic cloud-types: slightly cold-based stratiform cloud and cold-, warm- and very warm-based convective clouds. We discovered that the warmth of cloud-base, especially in the tropics, promotes SIP processes of raindrop-freezing fragmentation and rime-splintering, and surprisingly, sublimational breakup too. It was found that breakup in ice-ice collisions is ubiquitous. Finally, a portable laboratory chamber was constructed at Lund and deployed in northern Sweden to observe breakup in graupel-snow collisions outdoors. This was seen to be even more prolific than treated in our 2018 formulation. Papers describing results are either published or soon to be published.

54 ENVIRONMENTAL SCIENCES↗

The Cloud-Aerosol Transport System (CATS): a New Lidar for Aerosol and Cloud Profiling from the International Space Station

Spaceborne lidar profiling of aerosol and cloud layers has been successfully implemented during a number of prior missions, including LITE, ICESat, and CALIPSO. Each successive mission has added increased capability and further expanded the role of these unique measurements in wide variety of applications ranging from climate, to air quality, to special event monitoring (ie, volcanic plumes). Many researchers have come to rely on the availability of profile data from CALIPSO, especially data coincident with measurements from other A-Train sensors. The CALIOP lidar on CALIPSO continues to operate well as it enters its fifth year of operations. However, active instruments have more limited lifetimes than their passive counterparts, and we are faced with a potential gap in lidar profiling from space if the CALIOP lidar fails before a new mission is operational. The ATLID lidar on EarthCARE is not expected to launch until 2015 or later, and the lidar component of NASA's proposed Aerosols, Clouds, and Ecosystems (ACE) mission would not be until after 2020. Here we present a new aerosol and cloud lidar that was recently selected to provide profiling data from the International Space Station (ISS) starting in 2013. The Cloud-Aerosol Transport System (CATS) is a three wavelength (1064, 532, 355 nm) elastic backscatter lidar with HSRL capability at 532 nm. Depolarization measurements will be made at all wavelengths. The primary objective of CATS is to continue the CALIPSO aerosol and cloud profile data record, ideally with overlap between both missions and EarthCARE. In addition, the near real time data capability of the ISS will enable CATS to support operational applications such as air quality and special event monitoring. The HSRL channel will provide a demonstration of technology and a data testbed for direct extinction retrievals in support of ACE mission development. An overview of the instrument and mission will be provided, along with a summary of the science objectives and simulated data.

Welton, Ellsworth J.↗

The Cloud-Aerosol Transport System (CATS): A New Lidar for Aerosol and Cloud Profiling from the International Space Station

Spaceborne lidar profiling of aerosol and cloud layers has been successfully implemented during a number of prior missions, including LITE, ICESat, and CALIPSO. Each successive mission has added increased capability and further expanded the role of these unique measurements in wide variety of applications ranging from climate, to air quality, to special event monitoring (ie, volcanic plumes). Many researchers have come to rely on the availability of profile data from CALIPSO, especially data coincident with measurements from other A-Train sensors. The CALIOP lidar on CALIPSO continues to operate well as it enters its fifth year of operations. However, active instruments have more limited lifetimes than their passive counterparts, and we are faced with a potential gap in lidar profiling from space if the CALIOP lidar fails before a new mission is operational. The ATLID lidar on EarthCARE is not expected to launch until 2015 or later, and the lidar component of NASA's proposed Aerosols, Clouds, and Ecosystems (ACE) mission would not be until after 2020. Here we present a new aerosol and cloud lidar that was recently selected to provide profiling data from the International Space Station (ISS) starting in 2013. The Cloud-Aerosol Transport System (CATS) is a three wavelength (1064,532,355 nm) elastic backscatter lidar with HSRL capability at 532 nm. Depolarization measurements will be made at all wavelengths. The primary objective of CATS is to continue the CALIPSO aerosol and cloud profile data record, ideally with overlap between both missions and EarthCARE. In addition, the near real time (NRT) data capability ofthe ISS will enable CATS to support operational applications such as aerosol and air quality forecasting and special event monitoring. The HSRL channel will provide a demonstration of technology and a data testbed for direct extinction retrievals in support of ACE mission development. An overview of the instrument and mission will be provided, along with a summary of the science objectives and simulated data. Input from the ICAP community is desired to help plan our NRT mission goals and interactions with ICAP forecasters.

Welton, Ellsworth J.↗

pnnl/Lagrangian-Droplets

The Lagrangian Droplet cloud microphysics scheme uses high-resolution data of air velocity, temperature, and water vapor mixing ratio to drive particle-based simulations of cloud microphysics. The model was first implemented using a high-resolution Large Eddy Simulation of the Pi Chamber at Michigan Tech, which is small-scale convection chamber the generates clouds in a laboratory setting

Fierce, Laura [Pacific Northwest National Laborato↗

The Impact of Microphysical Schemes on Hurricane Intensity and Track

During the past decade, both research and operational numerical weather prediction models [e.g. the Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. WRF is a next-generation meso-scale forecast model and assimilation system. It incorporates a modern software framework, advanced dynamics, numerics and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At NASA Goddard, four different cloud microphysics options have been implemented into WRF. The performance of these schemes is compared to those of the other microphysics schemes available in WRF for an Atlantic hurricane case (Katrina). In addition, a brief review of previous modeling studies on the impact of microphysics schemes and processes on the intensity and track of hurricanes is presented and compared against the current Katrina study. In general, all of the studies show that microphysics schemes do not have a major impact on track forecasts but do have more of an effect on the simulated intensity. Also, nearly all of the previous studies found that simulated hurricanes had the strongest deepening or intensification when using only warm rain physics. This is because all of the simulated precipitating hydrometeors are large raindrops that quickly fall out near the eye-wall region, which would hydrostatically produce the lowest pressure. In addition, these studies suggested that intensities become unrealistically strong when evaporative cooling from cloud droplets and melting from ice particles are removed as this results in much weaker downdrafts in the simulated storms. However, there are many differences between the different modeling studies, which are identified and discussed.

Tao, Wei-Kuo↗

Aerosol-Radiation-Cloud Interactions in the South-East Atlantic: First Results from the ORACLES-2016 Deployment and Plans for Future Activities

Southern Africa produces almost a third of the Earths biomass burning (BB) aerosol particles. Particles lofted into the mid-troposphere are transported westward over the South-East (SE) Atlantic, home to one of the three permanent subtropical stratocumulus (Sc) cloud decks in the world. The SE Atlantic stratocumulus deck interacts with the dense layers of BB aerosols that initially overlay the cloud deck, but later subside and may mix into the clouds. These interactions include adjustments to aerosol-induced solar heating and microphysical effects, and their global representation in climate models remains one of the largest uncertainties in estimates of future climate. Hence, new observations over the SE Atlantic have significant implications for regional and global climate change predictions.Our understanding of aerosol-cloud interactions in the SE Atlantic is severely limited. Most notably, we are missing knowledge on the absorptive and cloud nucleating properties of aerosols, including their vertical distribution relative to clouds, on the locations and degree of aerosol mixing into clouds, on the processes that govern cloud property adjustments, and on the importance of aerosol effects on clouds relative to co-varying synoptic scale meteorology.We describe first results from various synergistic, international research activities aimed at studying aerosol-cloud interactions in the region:NASAs airborne ORACLES (ObseRvations of Aerosols Above Clouds and Their IntEractionS) deployment in AugustSeptember of 2016,the DoEs LASIC (Layered Atlantic Smoke Interactions with Clouds) deployment of the ARM Mobile Facility to Ascension Island (June 2016 October 2017), the ground-based components of CNRS AEROCLO-sA (Aerosols Clouds and Fog over the west coast of southern Africa), and ongoing regional-scale integrative, process-oriented science efforts as part of SEALS-sA (Sea Earth Atmosphere Linkages Study in southern Africa).We expect to describe experimental setups as well as showcase initial aerosol and cloud property distributions. Furthermore, we discuss the implementation of future activities in these programs in coordination with the UK Met Offices CLARIFY (CLoud-Aerosol-Radiation Interactions and Forcing) experiment in 2017.

aerosol↗

Jay : A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Abstract We present Jay , a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay 's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Silva, Joaquim↗

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations: 2. Retrieval Evaluation

An infrared-based optimal estimation (OE-IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A-Train) with Aqua. The results show that OE-IR cloud optical thickness (tau) and effective radius (r(sub eff)) retrievals perform best for ice clouds having 0.5 < tau< 7 and r(sub eff) < 50microns. For global ice clouds, the averaged retrieval uncertainties of tau and r(sub eff) are 19% and 33%, respectively. For optically thick ice clouds with tau larger than 10, however, the tau and r(sub eff) retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height (h), the averaged global uncertainty is 0.48km. Relatively large h uncertainty (e.g., > 1km) occurs for tau < 0.5. Analysis of 1month of the OE-IR retrievals shows large tau and r(sub eff) uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high-latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE-IR and the operational products show consistent tau and h retrievals. However, obvious differences between the OE-IR and the MODIS Collection 6 r(sub eff) are found.

cloud optics↗

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗

Dynamic Subgrid Turbulence Modeling for Shallow Cumulus Convection Simulations beyond LES Resolutions

A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC Cloud (MONC) model using two averaging flavors, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky–Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-gray-zone and gray-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale- and stability-dependent profile of the subfilter turbulence length scale across the chosen resolution range. At gray-zone resolutions the adaptive length scales can better represent the early precloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result, the initialization and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary layer, which fails to ventilate moisture from the boundary layer, resulting in the delayed spinup of the cloud layer. Moreover, strong downgradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for nonlocal transports, leading to overenergetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behavior.

54 ENVIRONMENTAL SCIENCES↗

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗

Atmospheric numerical modeling resource enhancement and model convective parameterization/scale interaction studies

Research will be undertaken in this contract in the area of Modeling Resource and Facilities Enhancement to include computer, technical and educational support to NASA investigators to facilitate model implementation, execution and analysis of output; to provide facilities linking USRA and the NASA/EADS Computer System as well as resident work stations in ESAD; and to provide a centralized location for documentation, archival and dissemination of modeling information pertaining to NASA's program. Additional research will be undertaken in the area of Numerical Model Scale Interaction/Convective Parameterization Studies to include implementation of the comparison of cloud and rain systems and convective-scale processes between the model simulations and what was observed; and to incorporate the findings of these and related research findings in at least two refereed journal articles.

Cushman, Paula P.↗

High-Resolution Modeling of the Dust and Water Cycles with the NASA Ames Mars Global Climate Model

NASA’s Mars Climate Modeling Center at Ames Research Center is currently undergoing an exciting period of growth in personnel, modeling capabilities, and science productivity. We are transitioning from our legacy Arakawa C-grid finite-difference dynamical core to the NOAA/GFDL cubed-sphere finite-volume dynamical core for simulating the climate of Mars in a global framework. This highly parallelized core is scalable and flexible, which allows for significant improvements in the horizontal and vertical resolutions of our simulations. We have implemented the Ames water ice cloud microphysics package described in Haberle et al. (2018) into this new dynamical core. We will present high-resolution simulations of the dust and water cycles that show that sub-degree horizontal resolution improves the agreement between the vertical distribution of dust and water ice and observations. In particular, both water ice clouds and dust are transported to higher altitudes due to stronger topographic circulations at high resolution. Preliminary results suggest that high-resolution global modeling is needed to properly capture critical features of the dust and water cycles, and thus the current Mars climate.

Kahre, Melinda A.↗

Cloud Computing Methods for Near Rectilinear Halo Orbit Trajectory Design

Complicated mission design problems require innovative computational solutions. As spacecraft depart from a proposed Gateway in a Near Rectilinear Halo Orbit (NRHO), recontact analysis is required to avoid risk of collision and ensure safe operations. Escape dynamics from NRHOs are governed by multiple gravitational bodies, yielding a trajectory design space that is exhaustively large. This paper summarizes the recontact analysis for departure from the NRHO and describes how the Deep Space Trajectory Explorer (DSTE) trajectory design software incorporates high performance cloud computing to compute and visualize the orbit design space. Recent focus on exploration missions to cislunar space has kindled accelerated interest in multibody orbit solutions. Trajectory analysis in the presence of multiple gravity fields is complex, and innovative computational tools are needed to simplify complicated design spaces, to generate large quantities of data quickly, and to visualize the output for user accessibility. The Gateway mission is a prime example. The Gateway1 is proposed as a human outpost in deep space. The current baseline orbit for the Gateway is a Near Rectilinear Halo Orbit (NRHO) near the Moon.2 The NRHO exists in a regime that experiences the gravitational effects of the Earth and the Moon simultaneously, complicating orbit analysis. The mission design process benefits greatly from updated computational tools for multibody missions like the Gateway. As an example, consider the problem of assessing the risk of collision in an NRHO. As a staging location to missions to the lunar surface and beyond the Earth-Moon system, the Gateway will experience spacecraft and other objects regularly arriving and departing. Departing objects potentially include spent logistics modules, visiting crew vehicles, debris objects, wastewater particles, and cubesats. Each departure is governed by the dynamics of the Gateway orbit and the surrounding dynamical environment. Over time, any unmaintained object in such an orbit eventually departs due to the small instabilities associated with the NRHOs. A separation maneuver speeds the departure from the NRHO, but the effects of the maneuver on the spacecraft behavior depend on the location, magnitude, and direction of the burn. Escape dynamics from the NRHO with regard to these maneuver options open up an enormous potential trajectory design space where subtle changes in input can produce dramatically large changes in the results. Any departing object must avoid recontacting the Gateway as it leaves the lunar vicinity, and a recontact analysis thus involves a significant number of computations and extensive output data. To explore the dynamics of this extensive design space, the Deep Space Trajectory Explorer3 (DSTE) trajectory design software incorporates new High Performance Computing (HPC) services and novel interactive visualizations. This paper details the HPC and cloud infrastructure techniques that are implemented in the DSTE, applying the new capabilities to analysis of recontact risk with the Gateway in NRHO. NEAR RECTILINEAR HALO ORBITS The Gateway is planned to fly in a lunar NRHO as its baseline orbit. The NRHO families of orbits are subsets of the larger halo families, which originate from planar orbits near the L1 and L2 libration points; the Earth-Moon L2 halo family appears in Figure 1. Each halo orbit is perfectly periodic in the Circular Restricted 3-Body Problem (CR3BP) and becomes a quasi-periodic orbit in a higher fidelity ephemeris force model. The NRHOs are defined as those members of the halo family with bounded stability properties;2 they pass near the Moon at perilune and are nearly polar. Families exist with apolunes located both above the lunar north pole and above the lunar south pole; the Gateway is planned to reside in a southern L2 NRHO in a 9:2 resonance with the lunar synodic period. The 9:2 NRHO is characterized by a period of about 6.5 days, a perilune radius of about 3,500 km, and an apolune radius of about 71,000 km; it is strongly affected by the gravity of both the Earth and the Moon simultaneously. This NRHO offers extended communications with assets on the south pole of the Moon,4 as well as low-cost orbit maintenance and attitude control,5 favorable eclipse avoidance properties,6 and inexpensive transfers from Earth and to other destinations.5,7 The NRHO portion of the southern L2 halo family is highlighted in black in Figure 1, and the 9:2 NRHO appears in blue.

Phillips, Sean M.↗

Revisiting GloSSAC Using Space Based Measurements

We revisit Global Space-based stratospheric aerosol climatology (GloSSAC) and extend the dataset through 2021 in the latest version (version 2.2). Several space-based measurements have been used to construct GloSSAC. For version 2.2, important changes include implementation of a revised aerosol/cloud categorization for The Stratospheric Aerosol and Gas Experiment (SAGE III/ISS). SAGE III/ISS began its mission in June 2017. While SAGEIII/ISS makes reliable and robust solar occultation measurements in stratosphere—similar to its predecessors, interpreting aerosol extinction measurements in the vicinity of tropopause and in the troposphere have been a challenge for all SAGE measurements. Here, we study the challenges associated with the discrimination of aerosols and clouds from the extinction measurements. Here, we describe the methods implemented to categorize Clouds and aerosols using available SAGEIII/ISS aerosol measurements. We use version 5.2 of SAGE III/ISS extinction coefficients for the analysis. The current algorithm now classifies standard (background) and non-standard (enhanced) aerosols in the stratosphere and identify enhanced aerosols and aerosol/cloud mixture in the tropopause region. Extinction coefficient measurements from SAGE series of observations make an important contribution in the GloSSAC data base and therefore, the impact of cloud-filtered aerosol extinction coefficient measurements on the latest version of GloSSAC (version 2.2) is also discussed. Additionally, we discuss minor version changes occurred in other space-based measurements that include Optical Spectrograph and InfraRed Imaging System(OSIRIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation(CALIPSO). We also plan on evaluating and using aerosol extinction profiles from other satellite data sets such as Ozone Mapping Profiler Suite (OMPS) in a future version.

stratospheric aerosol↗

Discrimination of Biomass Burning Smoke and Clouds in MAIAC Algorithm

The multi-angle implementation of atmospheric correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the smoke test to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 micrometers as compared to 0.47-0.67 micrometers due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007.

Lyapustin, A.↗