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NASA NTRSDate not supplied

Hail Storm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalyses

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience--our capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio. Hail CatModels are developed with climatologies that define hailstorm frequency and severity. Hail-prone areas can be defined using hail reports from trained spotters, the media, and the general public. Extremely severe hail (2+ inch diameter) occurs nearly every day across the world. Weather radars can detect hail because hailstones strongly reflect microwave signals that they emit. However, hail climatologies are difficult to derive because hail covers small areas and there are neither hail reporting mechanisms (e.g. website or mobile app) nor radar networks in most places outside the US and Europe. This lack of ground truth on severe hail puts society and economies at risk. Hail is generated within storms by strong updrafts. These updrafts exhibit unique signatures in NASA and other agency satellite observations, offering new opportunities for hailstorm analysis. Geostationary (GEO) visible and infrared imagery has been collected for ~15-25 years across the world (region dependent) and methods have been developed at NASA Langley Research Center (LaRC) to detect hailstorm updrafts using GEO imagery. Climatological GEO updraft data has been used by Willis Towers Watson (WTW), a leader in catastrophe risk assessment for the insurance industry, and Karlsruhe Institute of Technology to develop CatModels over Europe and Australia. Hail can also be inferred with passive microwave imagery collected by low-Earth-orbiting sensors such as the GPM GMI, TRMM TMI, AMSR-E, AMSR-2, SSM/I, and SSMIS over the last 20+ years using methods developed at the Marshall Space Flight Center (MSFC). Hailstorms generate enhanced lightning flash rates that can be tracked using new GOES-R series GEO Lightning Mapping (GLM) imagery. Atmospheric reanalyses can be used to define favorable hailstorm environments for combination with the satellite-based storm detections. This presentation will describe a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between LaRC and MSFC, WTW, and partners in Brazil, Argentina, and South Africa. This project seeks to mitigate hail disasters by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. GEOO visible and infrared metrics of storm intensity, environmental conditions based on reanalyses, spotter hail reports and radar MESH observations are intercompared to quantify the detectability of hailstorms, and our ability to discriminate hailstorms from other severe storms. We are also maturing methods using land surface imaging satellite data (e.g. MODIS, Landsat, Sentinel 1 and 2) to identify hail damage to agriculture. Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America and South Africa are developing insurance markets of interest to WTW clients, and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency. Project datasets will be made available via online GIS-enabled tools developed at the LaRC Atmospheric Science Data Center (ASDC) which will visualize data and provide it in multiple formats for use in a wide range of open source and commercial tools.

Kristopher Michael Bedka↗
NASA NTRS2020

Navigation Design and Operations of Maven Aerobraking

This paper describes the operational design and execution of the MAVEN aerobraking phase at Mars from a Navigation Team perspective. MAVEN was designed to perform atmospheric science in a ~150x6200 km altitude elliptical orbit. After the primary science mission, it was decided that MAVEN should circularize its orbit, as much as feasible from a spacecraft and mission standpoint, to better support relay operations with the landers. As a result, MAVEN performed aerobraking in the first half of 2019 to reduce its orbit to ~150x4500 km altitude. Although MAVEN did not decrease its altitude as low as previous aerobraking missions, it had several unique challenges. Science observations continued to be taken during aerobraking, requiring dramatically better Navigation accuracies than typical for such phases. Furthermore, continuous DSN coverage with 2-way Doppler data was not available. So, with 40% less Doppler data, Navigation had to meet prediction accuracies which were an order of magnitude smaller than in previous aerobraking operations. Spacecraft accelerometer data was included in Navigation analyses in order to meet these requirements.

Jakosky, Bruce↗
NASA NTRS2021

Aerosol–Ice Formation Closure: A Southern Great Plains Field Campaign

Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol-ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on co-located measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, that are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol-ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.

ice formation↗
NASA NTRS2022

Cloud Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) - Data Management - Data Products Catalog V4.94

The CALIPSO V2.00 Lidar Level 2 Polar Stratospheric Cloud data product is an updated version of an already order-able dat set. The changes were signed off by the CALIPSO Configuration Control Board, versioned, and the code uploaded to a code repository. There is no ITAR/SBU data or code associated with this product. Data will be publicly order-able at the NASA LaRC Atmospheric Sciences Data Center (ASDC). All documentation and web sites will be made public once the data product is released. The data is in HDF4 format and will be generated for majority of the mission (June 2006 - March 2021). The attached Data Products Catalog (v4.94) describes this new product in section 2.14, pp 115- 119.

Mark Vaughan↗
NASA NTRSDate not supplied

Experiences in the Practice of Design of Experiments at NASA

Statistical design of experiments (DOE) has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits DOE techniques by accelerating learning, maximizing knowledge, ensuring strategic resource investment, and informing data-driven decisions. In practice, DOE relies on multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to meet challenging research objectives. This presentation shares experiences in the practice of design of experiments at NASA in aeronautics, space exploration, and atmospheric science.

Peter A. Parker↗
NASA NTRS2022

EMIRS Observations of the Aphelion-Season Mars Atmosphere

Thermal infrared spectra taken by the Emirates Mars Infrared Spectrometer (EMIRS) on-board the Emirates Mars Mission (EMM) spacecraft are well suited for the retrieval of surface temperatures, the atmospheric temperature profile from the surface to ∼40 km, and the column abundance of dust aerosols, water ice clouds, and water vapor. A constrained linear inversion retrieval routine that includes multiple scattering has been developed and optimized for this purpose. Here, we present a brief overview of the retrieval algorithm and first atmospheric science results from observations taken by EMIRS over the first Earth year of EMM Science Phase operations. These retrievals show extensive water ice clouds, typical for the aphelion season of these observations, and the expected north polar summer maximum and subsequent equatorward transport of water vapor is well documented. An unusually strong and early regional dust storm and its associated thermal response were also observed.

Michael D. Smith↗
NASA NTRSDate not supplied

Statistical Engineering

This webinar provides an overview of the International Statistical Engineering Association (ISEA), and it illustrates the practice of statistical engineering at NASA. ISEA was formed to promote the study of how successful data-based problem-solving methods are leveraged to realize innovative opportunities and solve problems sustainably. ISEA is comprised of statisticians, engineers, scientists, and other professionals that exchange ideas and experiences in the development and application of statistical engineering theories. ISEA is building the body of knowledge of the statistical engineering discipline with a particular focus on improving academic preparation for tackling complex problems. Over the past 15 years, the practice of statistical engineering has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits from an application-focused statistical engineering perspective to accelerate learning, maximize knowledge, ensure strategic resource investment, and inform data-driven decisions. The second portion of this presentation provides an overview of infusing statistical engineering at NASA through pioneering case studies in aeronautics, space exploration, and atmospheric science.

Peter A Parker↗
NASA NTRSDate not supplied

The Relationship Between Two Methods for Estimating Uncertainties in Data Assimilation

This note examines the relationship between two apparently unrelated methods for estimating error statistics or uncertainties of relevance to data assimilation. The first method is due to (Desroziers et al., 2005, Q. J. R. Meteorol. Soc., 131, 3385–3396; referred to as DBCP hereafter) and relies on residual statistics readily available from data assimilation applications. The second method, the three- cornered hat (3CH) developed by Gray and Allan (1974, IEEE 28th Annual Symp. Freq. Control, 243–246), only recently applied to atmospheric sciences, uses three data sets and can derive estimates of relevant error uncertainties as well. The usefulness of both methods lies in them not requiring knowledge of the true value of the quantities at play. DBCP derives its results by relying explicitly on the constraints as- sociated with the data assimilation minimization problem; 3CH is general and its estimates hold as long as errors in the three data sets of choice are uncorrelated. Establishing the relationship between the methods requires applying the 3CH approach to the same observation, background, and analysis data sets used by DBCP. In this case, the same assumptions of DBCP on residual errors allow for cancellation of error cross–covariance terms in 3CH such that two of its corners derive identical estimates for observation and background error covariances as those of DBCP. The error cross–covariance terms associated with the third corner are shown to add up to twice the analysis error covariance so that the 3CH estimate for the third corner recovers the negative of the analysis error covariance. Illustrations of these findings are provided by deriving uncertainties for radio occultation bending angles.

Ricardo Todling↗
NASA NTRS2017

The HITRAN2016 Molecular Spectroscopic Database

This paper describes the contents of the 2016 edition of the HITRAN molecular spectroscopic compilation. The new edition replaces the previous HITRAN edition of 2012 and its updates during the intervening years. The HITRAN molecular absorption compilation is composed of five major components: the traditional line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, infrared absorption cross-sections for molecules not yet amenable to representation in a line-by-line form, collision-induced absorption data, aerosol indices of refraction, and general tables such as partition sums that apply globally to the data. The new HITRAN is greatly extended in terms of accuracy, spectral coverage, additional absorption phenomena, added line-shape formalisms, and validity. Moreover, molecules, isotopologues, and perturbing gases have been added that address the issues of atmospheres beyond the Earth. Of considerable note, experimental IR cross-sections for almost 300 additional molecules important in different areas of atmospheric science have been added to the database. The compilation can be accessed through www.hitran.org. Most of the HITRAN data have now been cast into an underlying relational database structure that offers many advantages over the long-standing sequential text-based structure. The new structure empowers the user in many ways. It enables the incorporation of an extended set of fundamental parameters per transition, sophisticated line-shape formalisms, easy user-defined output formats, and very convenient searching, filtering, and plotting of data. A powerful application programming interface making use of structured query language (SQL) features for higher-level applications of HITRAN is also provided.

HITRAN↗
NASA NTRS2023

Cloud - Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) - Data Management System: Data Products Catalog V4.95

The CALIPSO V4.51 Lidar Level 1 and Level 2 data product is an updated version of an already order-able dataset. The changes were signed off by the CALIPSO Configuration Control Board, versioned, and the code uploaded to a code repository. There is no ITAR/SBU data or code associated with this product. Data will be publicly order-able at the NASA LaRC Atmospheric Sciences Data Center (ASDC). All documentation and web sites will be made public once the data product is released. The data is in HDF4 format and will be generated for majority of the mission (June 2006 - August 2023). The attached Data Products Catalog (v4.95) describes the content of these new data products.

Mark Vaughan↗
NASA NTRSDate not supplied

Air Quality (AQ) Monitoring From Space By NASA Using Tempo

In an era marked by escalating environmental concerns, understanding the Earth's atmosphere and its complex interactions is paramount. The Tropospheric Emission Monitoring of POllution (TEMPO) instrument is a cutting-edge venture by NASA that stands at the forefront of Earth observation technology and exemplifies NASA's commitment to unraveling the intricacies of the air we breathe. TEMPO was launched with the primary objective of monitoring air quality. This story map explores the innovative technology behind the TEMPO instrument, its mission objectives, tools and services provided by the Atmospheric Science Data Center (ASDC) for data access and the potential impact of TEMPO data findings on our health and Earth's environmental future.

Hazem Mahmoud↗
NASA NTRSDate not supplied

Multidimensional Data Aggregation in the Cloud with Application to Geostationary Satellite-based Air Quality Monitoring

Scientists use satellite data for studying Earth's systems, and the remote sensing data that these satellites collect are typically separated into files of a size small enough for efficient network transfer and storage. However, researchers usually prefer to analyze the data based on real-world dimensions like time, space, or elevation. To help with this, NASA's Atmospheric Science Data Center (ASDC) developed a new cloud-based tool that combines these smaller data chunks into larger, more useful datasets. The tool works on Network Common Data Form (netCDF4) and some HDF5 formatted files, and it is available as a service in NASA's Earthdata Cloud. In this presentation, we showcase this service using data from the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. By combining TEMPO's continuous observations over time, we create longer and more informative analysis-ready time series to facilitate the study of air quality patterns. Insights gained will provide a more comprehensive understanding of pollution sources, transport patterns, and their effects on the environment and human health.

Daniel Kaufman↗
NASA NTRS2024

Leveraging Scientific Community Knowledge for Air Quality Model Chemistry Parameterizations

Air pollution contributes to adverse health outcomes. Approximately 121 million people in the United States—one third of the population—live where National Ambient Air Quality Standards (NAAQS) are violated. In most cases, the criteria pollutants exceeding standards are ozone (O 3 ) and fine particles (PM 2.5 ). In addition, 188 substances known or suspected to cause cancer or other serious health effects are designated as hazardous air pollutants (HAPs). Essentially, all O 3 and significant portions of PM 2.5 and HAPs are produced in the atmosphere through chemical and physical processes. In the case of PM 2.5 , subcomponents formed primarily from precursor gases—sulfate, nitrate, ammonium, and secondary organic aerosol (SOA)—account for 60% of the U.S. county-level annual mean concentration. In addition, 47% of the cancer risk and 25% of the noncancer risk from HAPs have been attributed to atmospheric chemistry rather than direct emissions. In this article, we introduce the role of chemical mechanisms in air quality models, a new atmospheric science community effort, and needs for further mechanism development.

Havala O.T. Pye↗
NASA NTRSDate not supplied

Statistical Practice and Research at NASA

The discipline of statistics has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact and value. In aerospace research and development, it accelerates learning, maximizes knowledge, ensures strategic resource investment, and informs rigorous data-driven decisions. In practice, it requires immersive multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to address challenging research objectives. This presentation provides an overview of statistical case studies in aeronautics, space exploration, and atmospheric science, and it highlights statistical research motivated by NASA’s challenging applications.

Peter A. Parker↗
NASA NTRSDate not supplied

Enabling Analysis of Air Quality Data From Tropospheric Emissions: Monitoring of POllution (Tempo) Via Cloud-Based Tools

Launched in April 2023, the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument provides high-resolution measurements of key atmospheric pollutants, such as ozone, nitrogen dioxide, and formaldehyde. Maximizing the use and utility of this new source of air quality information requires streamlining data access for a wide variety of research, public health, and other interested users. These varied applications often require the data to be structured in different ways, e.g., specific formats, array shapes, or file sizes. To enable access to TEMPO data in different forms, the NASA Atmospheric Science Data Center (ASDC), as part of the NASA Earth Science Data and Information System (ESDIS), provides a variety of cloud-based data transformation and GIS visualization tools. This presentation demonstrates methods of accessing and working with TEMPO data through these services, while highlighting aspects of the software and algorithmic workflows that perform the necessary data transformations. Examples include data subsetting, concatenation, and visualizations accessible via Jupyter notebooks and GIS software.

Daniel Kaufman↗
NASA NTRSDate not supplied

June 2023 Canadian Wildfire Effects on Hampton Roads VA using ASDC Data

In June of 2023 thousands of acres of wildfires spread across northwestern Canada. This event emitted huge amounts of smoke, and pollutants that travelled east across the North America, the Atlantic ocean and eventually all the way to Continental Europe. This smoke created a dangerous air quality event throughout the Atlantic Coast. As climate change increases the prevalence of wildfires more cities and states will be affected by the dangerous smoke and pollutants produced by these events. In this poster we documented and investigated the June 06th Air Quality Event over Hampton Roads, VA. To perform a comparative analysis, we utilized two data sources: NASA’s Atmospheric Science Data Center we used the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), the Deep Space Climate ObserVatoRy (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC), data product, and the EPA’s ground-based Air Quality System (AQS).

Air Quality↗
NASA NTRSDate not supplied

PREFIRE at ASDC Revolutionizing Polar System Observations

In the ongoing battle against climate change, early detection is key to minimizing their devastating impact on communities and ecosystems. Hosted by the Atmospheric Science Data Center (ASDC) at NASA's Langley Research Center, PREFIRE mission aims to bridge a critical knowledge gap in climate science by focusing on the far-infrared (FIR) portion of the electromagnetic spectrum, which has been historically under-observed.

Hazem M↗