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NASA NTRS2012

Sao Paulo Lightning Mapping Array (SP-LMA): Deployment, Operation and Initial Data Analysis

An 8-10 station Lightning Mapping Array (LMA) network is being deployed in the vicinity of Sao Paulo to create the SP-LMA for total lightning measurements in association with the international CHUVA [Cloud processes of the main precipitation systems in Brazil: A contribution to cloud resolving modeling and to the GPM (Global Precipitation Measurement)] field campaign. Besides supporting CHUVA science/mission objectives and the Sao Luiz do Paraitinga intensive operation period (IOP) in November-December 2011, the SP-LMA will support the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), both sensors on the NOAA Geostationary Operational Environmental Satellite-R (GOES-R), presently under development and scheduled for a 2015 launch. The proxy data will be used to develop and validate operational algorithms so that they will be ready for use on "day1" following the launch of GOES-R. A preliminary survey of potential sites in the vicinity of Sao Paulo was conducted in December 2009 and January 2010, followed up by a detailed survey in July 2010, with initial network deployment scheduled for October 2010. However, due to a delay in the Sao Luiz do Paraitinga IOP, the SP-LMA will now be installed in July 2011 and operated for one year. Spacing between stations is on the order of 15-30 km, with the network "diameter" being on the order of 30-40 km, which provides good 3-D lightning mapping 150 km from the network center. Optionally, 1-3 additional stations may be deployed in the vicinity of Sao Jos dos Campos.

Blakeslee, R.↗

NASA NTRS2013

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011in the vicinity of São Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. The SP-LMA data also will be intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment and analyses for intercomparison studies and GOES-R proxy activities

Blakeslee, R. J.↗

NASA NTRS2017

Active and Passive 3D Vector Radiative Transfer with Preferentially-Aligned Ice Particles

For the purposes of interpreting active (radar) and passive (radiometer) microwave and millimeter wave remote sensing data, we have constructed a consistent radiative transfer modeling framework to simulate the responses for arbitrary sensors with differing sensing geometries and hardware configurations. As part of this work, we have implemented a recent method for calculating the electromagnetic properties of individual ice crystals and snow flakes. These calculations will allow us to exploit polarized remote sensing observations to discriminate different particles types and elucidate dynamics of cloud and precipitating systems.

Adams, Ian S.↗

NASA NTRS2018

Particle Size Calibration Testing in the NASA Propulsion Systems Laboratory

The particle size characterization portion of the 2017 Propulsion Systems Laboratory (PSL) Cloud Calibration is described. The work focuses on characterizing the particle size distribution of the icing cloud as a function of simulated atmospheric conditions.These results will aid in upcoming ice crystal and supercooled liquid icing tests in PSL. Measurements acquired with the Phase Doppler Interferometer and High Speed Imaging instruments are presented. Experimental results indicate that the particle size distribution is primarily a function nozzle air and water pressures, and that air speed is not a significant effect for ice crystal clouds in PSL and both thermodynamic conditions and air speed are not significant effects for supercooled liquid water clouds in PSL.

Engine icing↗

NASA NTRS2019

Particle Size Calibration Testing in the NASA Propulsion Systems Laboratory

Engine Icing↗

NASA NTRS2018

A34G-07: Aerosol vertical distribution observed by both CALIPSO and CATS

We analyzed the aerosol vertical distribution in both middle and high latitude observed by two space-based lidar instruments: NASA's CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and CATS (Cloud-Aerosol Transport System) in 2015-2017. Since the two hosting spacecrafts make turns at different latitudes, where the lidars are able to make more frequent observations, combination of the two datasets provide a unique opportunity to compile a high frequency aerosol vertical distribution measurement at both mid-latitude and high latitude. Even though the two sensors have intrinsic differences, the overlapping part of their tracks provides opportunity for crosschecking and calibration. In addition to total aerosol, we also calculated the vertical distribution of major aerosol types in polar region (receptor), and compared them to that over the mid latitudes (source regions). The difference indicates the efficiency of polar-wise transport of various aerosol types.

Tan, Qian↗

NASA NTRS2020

Observation and Quantification of Aerosol Outflow from Southern Africa Using Spaceborne Lidar

Biomass burning in Africa provides a prolific source of aerosols that are transported from the source region to distant areas, as far away as South America and Australia. Models have long predicted the primary outflow and transport routes. Over time, field studies have validated the basic production and dynamics that underlie these transport patterns. In more recent years, the advancement of spaceborne active remote sensing techniques has allowed for more detailed verification of the models and, importantly, verification of the vertical distribution of the aerosols in the transport regions, particularly with respect to westerly transport over the Atlantic Ocean. The Cloud-Aerosol Transport System (CATS) lidar on the International Space Station has detection sensitivity that provides observations that support long-held theories of aerosol transport from the African subcontinent over the remote Indian Ocean and as far downstream as Australia.

Lidar↗

NASA NTRS2017

A Science-Driven Mission Concept to An Exoplanet

A concept for a science-driven robotic mission to an exoplanet was developed by a team of scientists and engineers from NASA and academia. The concept and scope were based on key mission and science requirements designed to address the question: “What makes a flight mission to an exoplanet compelling, in terms of science return, compared to what we will be able to learn in the next few decades with large near-Earth telescopes or other remote sensing techniques such as a telescope at the Solar Gravity Lens Focus?” By thinking systematically through mission and science goals and objectives, key requirements were developed that would drive technology developments in all necessary aspects, not just on propulsion. Unique science measurements would be performed en route to the exoplanet, including exploring the environment in the outer regions of our solar system, the Oort Cloud, the local interstellar medium, and the astrospheric environment around the host star. One of the key mission science objectives, and one that addresses why a mission to an exoplanet is compelling, was to confirm and characterize life. This objective is fundamental and drives the need for a precursor exoplanet characterization program to search for Earth-centric biosignatures and also drives key aspects of the mission concept. The team concluded that a direct confirmation of life would require in situ observations and measurements which cannot be performed on a fast (~10% of the speed of light) flyby; thus, the mission would require a method to slow down, orbit, or send a probe to the exoplanet’s surface. This capability drives a trade between interstellar travel velocity, trip duration, and propulsion architecture, as well as a high level of onboard autonomy, including adaptive science data collection, on-board data processing, and analysis. This paper describes our mission concept, the key requirements, and open trades.

Bennett, Gary↗

NASA NTRS2019

Surface Polarized Reflectance Analysis For Aerosol Remote Sensing

We study the Earth surface polarized reflectance using data collected by a space-based lidar. Accurate modelling of the surface reflectance supports retrieval algorithm development for the current and future Earth Science missions. Strong polarization of the laser light from Cloud-Aerosol Transport System (CATS) instrument, operated in 2015-2017, and nighttime measurements yield higher signal-to-noise ratio for polarization compared to the previous analysis of reflected, initially unpolarized, solar light.

Earth surface polarized reflectance↗

NASA NTRS2021

Planetary Boundary Layer Height Estimates From ICESat-2 and CATS Backscatter Measurements

The lowest layer of the atmosphere in which all human activity occurs is called the Planetary Boundary Layer (PBL). All physical interactions with the surface, such as heat and moisture transport, pollution dispersion and transport happen in this relatively shallow layer. The ability to understand and model the complex interactions that occur in the PBL is very important to air quality, weather prediction and climate modeling. A fundamental and physically important property of the PBL is its thickness or height. This work presents two methods to obtain global PBL height using satellite lidar data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and the Cloud-Aerosol Transport System (CATS). The first method is a straightforward backscatter threshold technique and the second is a machine learning approach known as a Convolutional Neural Network. The PBL height retrievals from the two methods are compared with each other and with PBL height from the NASA GEOS MERRA-2 reanalysis. The lidar-retrieved PBL heights have a high degree of spatial correlation with the model heights but are generally higher over ocean (∼400 m) and over northern hemisphere high latitude regions (∼1,000 m). Over mid-latitude and tropical land areas, the satellite estimated PBL heights agree well with model mid-day estimates. This work demonstrates the feasibility of using satellite lidar backscatter measurements to obtain global PBL height estimates, as well as determining seasonal and regional variability of PBL height.

Stephen P Palm↗

NASA NTRSDate not supplied

Containerized GEOS: Toward a Portable Climate Model

The NASA Goddard Earth Observing System (GEOS) is an Earth system model used for weather, climate, and other scientific applications. GEOS consists of linked components that can run in various configurations such as atmosphere-only and coupled atmosphere-ocean. Running this model on any new supercomputing system depends on operating systems, compilers, MPI stacks, and libraries being present and correctly configured. To remove that burden from users, our project explores building and running GEOS using Singularity containers – files containing all the needed software dependencies – on both NASA high-end computing systems and commercial cloud computing environments. Ultimately, the goal is for containerized GEOS to make it easier for users outside of NASA to deploy and run the model on any machine.

Matthew Thompson↗

NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that aims to foster collaborations amongst these three major aerosol specialties. It consists of a collection of interdisciplinary and independently funded projects/topics with clearly-defined goals. Currently there are four MIRA topic areas, including Satellite-Assisted Particulate Matter (SAPM), which is focused specifically on fine particulate matter (PM2.5). The study of PM2.5 is important due to its significant contribution to air pollution and resultant, adverse human health effects. The goal of the SAPM topic group is to conduct and provide intercomparisons of various methods and techniques to obtain surface PM2.5 estimates assisted by satellite remote sensors, global aerosol models, and in situ aerosol measurements. Some benefits of space-based/model-assisted PM2.5 retrievals include providing PM2.5 approximations in regions with poor in situ ground station coverage and a means for assessing spatial/temporal PM2.5 variations on regional and/or global scales. Furthermore, benefits of spaceborne lidar/model-assisted PM2.5 retrievals include providing nighttime PM2.5 estimates and characterization of near-surface aerosol vertical structure. SAPM is seeking international datasets of mass scattering/absorption coefficient and aerosol hygroscopic properties for different aerosol species, as well as ground-based in situ PM2.5 measurements. In this poster presentation, we give an overview of SAPM, and show results from three recent/ongoing PM2.5 studies. These efforts involve deriving surface PM2.5 concentrations from two spaceborne lidars, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud-Aerosol Transport System (CATS), and investigating key variables that impact the estimation of PM2.5 from model simulated aerosol optical depth (AOD) using the Goddard Earth Observing System (GEOS) model.

Travis D. Toth↗

NASA NTRS2024

Opinion: Aerosol Remote Sensing Over the Next Twenty Years

More than two decades ago, aerosol remote sensing underwent a revolution with the launch of the Terra and Aqua satellites. Advancement continued via additional launches carrying new passive and active sensors. Capable of retrieving parameters characterizing aerosol loading, rudimentary particle properties and in some cases aerosol layer height, the satellite view of Earth’s aerosol system came into focus.The modeling communities have made similar advances. Now the efforts have continued long enough that we can see developing trends in both remote sensing and modeling communities, allowing us to speculate about the future and how the community will approach aerosol remote sensing twenty years from now. We anticipate technology that will replace today’s standard multi-wavelength radiometers with hyperspectral and/or polarimetry all viewing in multiple angles.These will be supported by advanced active sensors with the ability to measure profiles of aerosol extinction in addition to backscatter. The result will be greater insight into aerosol particle properties. Algorithms will move from being primarily physically-based to include an increasing degree of Machine Learning methods, but physically-based techniques will not go extinct. However, the practice of applying algorithms to a single sensor will be in decline. Retrieval algorithms will encompass multiple sensors and all available ground measurements into a unifying framework, and these inverted products will be ingested directly into assimilation systems, becoming “cyborgs”: half observations, half model. In twenty years we will see a true democratization in space with nations large and small, private organizations and commercial entities of all sizes launching space sensors. With this increasing amount of data and aerosol products available, there will be a lot of bad data. User communities will organize to set standards and the large national space agencies will lead the effort to maintain quality by deploying and maintaining validation ground networks and focused field experiments. Through it all, interest will remain high in the global aerosol system and how that system affects climate, clouds, precipitation and dynamics, air quality, the environment and public health, transport of pathogens and fertilization of ecosystems, and how these processes are adapting to a changing climate.

aerosol remote sensing↗

NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote Sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that provides a forum for collaborations amongst these three atmospheric aerosol communities. MIRA consists of a collection of interdisciplinary and independently funded Topic/Project groups with clear goals and generally characterized by requests for additional scientific data. The Satellite-Assisted Particulate Matter (SAPM) Topic group focuses on the study of fine particulate matter (PM2.5), as it is a major contributor to air pollution and negatively impacts human health. Our group aims to provide intercomparisons of various methods and techniques for estimating surface PM2.5 assisted by satellite remote sensors (passive and active), global aerosol models, and in situ aerosol measurements. The overall motivation of our work is to enhance PM2.5 coverage beyond in situ ground stations, which can be limited. In this presentation, we provide an overview of various PM2.5 estimation approaches by current SAPM team members, as each approach has its own strengths and limitations. One approach uses near-surface aerosol extinction retrievals from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and an assumed value of mass extinction efficiency to derive PM2.5 concentrations (Toth et al 2019). A study using this method found a promising agreement (R=0.60; slope=0.89) with U.S. Environmental Protection Agency (EPA) in situ PM2.5 measurements over the contiguous United States (CONUS) for a 12-year (2007-2018) period (Toth et al 2022). In a different approach combining spaceborne lidar and a model, the Cloud Aerosol Transport System (CATS) lidar and Goddard Earth Observing System (GEOS) model are blended in an ensemble variational assimilation scheme to retrieve aerosol extinction and convert speciated mass concentrations to total PM2.5. A recent study (Matus et al in prep.) found agreement within 2 μg/m3 over the CONUS on the 2016 annual mean when compared to EPA ground-based measurements. Another study explored trends in city aerosol optical depth (AOD) from a spaceborne passive remote sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and found that these trends agree well with trends in city surface PM2.5, which provides confidence in the use of city AOD for city PM2.5 trend studies (Vohra et al 2021). In a follow-on study, steep and significant trends (~2.5 to 7.8% a-1) in AOD are found for several South Asian cities (e.g., Bangalore and Hyderabad), suggesting rapid growth in PM2.5 (Vohra et al 2022). Also in this presentation, we report results on our current collective SAPM study of India, a country that exhibits high levels of PM2.5 pollution. We show the temporal (i.e., seasonal) and spatial variability of PM2.5 over India using the CALIOP and CATS+model approaches, comparisons between the estimated PM2.5 from the retrieval schemes, and validation with in situ PM2.5 ground-based observations from the Central Pollution Control Board in India. This region provides us with an excellent case study to test our PM2.5 retrievals in heavily polluted scenes and over a complex and varying topography. The SAPM Topic group actively seeks international participants/collaborators in our group, including those working with in situ aerosol measurements (e.g., ACTRIS). We are interested in aerosol datasets in order to either improve our PM2.5 estimates from various approaches (e.g., using in situ mass scattering/absorption coefficient and aerosol hygroscopic properties for various aerosol species) and/or validate the PM2.5 estimates (e.g., using in situ ground-based PM2.5 concentrations).

Travis D Toth↗

NASA NTRSDate not supplied

Providing Satellite-Assisted Particulate Matter (SAPM) Estimates for the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group

The Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group is an international forum that promotes collaboration amongst researchers of these three atmospheric aerosol communities. MIRA currently consists of a collection of five interdisciplinary and independently funded Topic groups with focused goals and generally involve requests for additional scientific datasets. Participants of the MIRA Satellite-Assisted Particulate Matter (SAPM) Topic group study particulate matter with diameters smaller than 2.5 microns (PM2.5), because of its substantial role in air pollution and the resulting negative impacts on human health. SAPM seeks to provide intercomparisons of different methods and techniques for obtaining surface PM2.5 proxies using spaceborne passive and active remote sensors, aerosol models, and in situ observations. The ultimate motivation of SAPM is to provide greater PM2.5 coverage than in situ ground stations, which are limited in some areas of the contiguous United States and large regions throughout the globe. In this poster presentation, we provide an overview of the PM2.5 estimation techniques applied by current SAPM team members, as well as the strengths and limitations of each technique. These approaches include spaceborne lidar (Cloud-Aerosol Lidar with Orthogonal Polarization) only, combined spaceborne lidar (Cloud Aerosol Transport System) and a model (Goddard Earth Observing System), and using aerosol optical depth (AOD) from a spaceborne passive remote sensor (Moderate Resolution Imaging Spectroradiometer) to explore city trends in AOD and relate those to city trends in PM2.5. Also in this poster presentation, we highlight results of our SAPM study of India, a country characterized by high levels of PM2.5 concentrations. We show the temporal and spatial variability of PM2.5 over India derived using various approaches and validation with ground-based in situ PM2.5 observations. The SAPM Topic group actively seeks additional collaborators, including those working with in situ aerosol measurements, and is interested in aerosol datasets to improve and/or validate the PM2.5 estimates.

Travis D Toth↗

NASA NTRSDate not supplied

Developing Satellite-Assisted Particulate Matter (SAPM) Estimates over India for the MIRA Working Group

The Models, In Situ, and Remote Sensing of Aerosols (MIRA) Working Group is an international collective that encourages collaboration among researchers from these three atmospheric aerosol communities. MIRA currently comprises five interdisciplinary and independently funded Topic Groups, each with specific goals, and involves requests for additional scientific datasets. The Satellite-Assisted Particulate Matter (SAPM) Topic Group, as part of MIRA, focuses on studying particulate matter smaller than 2.5 microns in diameter (PM2.5) due to its significant contribution to air pollution and its harmful effects on human health. While the annual mean PM2.5 levels are typically low (~5-15 μg/m³) across most of the contiguous United States (CONUS), other countries experience much higher concentrations (e.g., India). SAPM aims to compare different methods and techniques for obtaining surface PM2.5 proxies using spaceborne passive and active remote sensors, aerosol models, and in situ measurements. Ultimately, SAPM aims to provide more extensive coverage of PM2.5 concentrations than what is currently available from in situ ground stations, which are limited in some parts of the CONUS and large regions worldwide. Current SAPM members are exploring PM2.5 estimation techniques using active sensors. This presentation offers an overview of these techniques and highlights the strengths and limitations of each approach. These techniques include 1) spaceborne lidar (CALIOP: Cloud-Aerosol Lidar with Orthogonal Polarization) alone, and 2) a combination of spaceborne lidar (CATS: Cloud Aerosol Transport System) and a global aerosol transport model (GEOS: Goddard Earth Observing System). Additionally, we present a case study featuring our SAPM research in India, a country with high levels of PM2.5 concentrations (i.e., state-level annual means of ~100-200 μg/m³). Consistent spatial patterns in PM2.5 over India are found from the in situ data, CALIOP-based, and CATS/model-based methods, with the highest concentrations found in northern India near New Delhi. The gridded PM2.5 analysis yields high R values between in situ and CATS/model (~0.7) and between in situ and CALIOP nighttime (~0.9), as well as good agreement between CATS/model and CALIOP nighttime PM2.5 estimates (R = ~0.8 and slope = ~0.9). For current and future efforts, the SAPM Topic Group is actively seeking new collaborators, especially those working with in situ aerosol measurements, and is interested in acquiring additional aerosol datasets to improve and validate the PM2.5 proxies.

Travis D Toth↗

DOE OSTI2023

NREL Project CloudZero

The National Renewable Energy Laboratory's (NREL's) CloudZero project is evaluating the ability to reliably and securely manage complex energy systems from the cloud, identifying major technical and regulatory barriers to cloud adoption, suggesting new security controls and best practices for cloud applications, and empowering industry to embrace the cloud, where appropriate.

cloud↗

DOE OSTI2023

Characterizing the Variation and Covariation of Cloud Microphysical Properties and Implications for Simulation of Subgrid-scale Warm-Rain Processes in Earth System Models (Final DOE-ASR Report)

Warm marine boundary layer (MBL) clouds constitute an important component in the global climate system, and precipitation plays a central role in controlling the water budget, radiative effects, and lifetime of these MBL clouds. Unfortunately, because of the relatively coarse effective grid resolution of the current generation of Earth system models (ESMs), the variety of cloud microphysical processes occurring inside an ESM grid cell are often oversimplified or unconstrained by observations. For example, the warm rain processes (e.g., autoconversion and accretion) are usually parameterized as nonlinear functions of grid-mean cloud properties. Because of the nonlinear nature of these functions, neglecting variability within the ESM grid volume can lead to substantial biases in precipitation production, cloud cover, and surface radiative fluxes. In state-of-the art ESMs, the influence of subgrid-scale variability is represented as an enhancement factor (EF) coefficient to the autoconversion, and accretion rates calculated from the model variables. However, EF is typically taken to be a constant or even used as a knob to tune model cloud properties to match observations, an ad hoc approach that may yield a desired cloud outcome yet introduce compensating errors. In this project, we used the combination of in situ cloud microphysics measurements from the ACE-ENA field campaign and large-eddy simulations (LES) to characterize and understand subgrid-scale variations and co-variations of cloud microphysical properties and use the results to evaluate and improve the representation of subgrid warm-rain processes in ESMs, in particular the EF used to tune the autoconversion and accretion processes. In this final report, we summarize our research activities and main findings in Section 2, provide a list of publications (Section 3) and presentations (Section 4) resulted from our research, and briefly discuss the student activities supported by this project.

54 ENVIRONMENTAL SCIENCES↗