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John E Yorks

Publications and source records attributed to John E Yorks.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Synergistic Retrievals of Ice in High Clouds From Elastic Backscatter Lidar, Ku-band Radar and Submillimeter Wave Radiometer Observations

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and sub-millimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of IceWater Content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments e.g. frequencies, sensitivities, etc. are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, out of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-Means clustering algorithm. In the second step, the IWC profile is estimated using an Ensemble Kalman Smoother (EKS) algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

Mircea Grecu↗
NASA NTRS2022

Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS): the 2022 Deployment

The Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS) is a NASA-supported field campaign to study snowstorms particularly over the northeastern United States. Snowfall within these winter storms is often organized in banded structures that can vary on multiple scales. The goals of IMPACTS are to characterize the spatial and temporal scale of snowbands, understand the processes controlling the structure and evolution of the bands and apply this knowledge to improving remote sensing and numerical modeling. IMPACTS takes place over three winter seasons, 2020, 2022 and 2023. IMPACTS flies two aircraft: the ER-2 equipped with satellite-simulating remote sensing instruments; and the P-3 equipped with in situ microphysics probes and environmental instrumentation. Stationary and mobile radar facilities and mobile sounding teams round out the observational assets. The preliminary results from the 2022 deployment are highlighted here.

Lynn A McMurdie↗
NASA NTRS2022

Aerosol Detection from the Cloud Aerosol Transport System on the International Space Station: Algorithm Overview and Implications for Diurnal Sampling

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia.

Edward P Nowottnick↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events, and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, enable detection of atmospheric features during daytime operations with a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS2020

Sensitivities in Satellite Lidar‐Derived Estimates of Daytime Top‐of‐the‐Atmosphere Optically Thin Cirrus Cloud Radiative Forcing: A Case Study

An optically thin cirrus cloud was profiled concurrently with nadir‐pointing 1,064 nm lidars on 11 August 2017 over eastern Texas, including NASA's airborne Cloud Physics Lidar (CPL) and space‐borne Clouds and Aerosol Transport System (CATS) instruments. Despite resolving fewer (37% vs. 94%) and denser (i.e., more emissive) clouds (average cloud optical depth of 0.10 vs. 0.03, respectively), CATS data render a near‐equal estimate of the top‐of‐atmosphere (TOA) net cloud radiative forcing (CRF) versus CPL. The sample‐relative TOA net CRF solved from CPL is 1.39 W/m2, which becomes 1.32 W/m2 after normalizing by occurrence frequency. Since CATS overestimates extinction for this case, the sample‐relative TOA net forcing is ~3.0 W/m2 larger than CPL, with the absolute value reduced to within 0.3 W/m2 of CPL due its underestimation of cloud occurrence. We discuss the ramifications of thin cirrus cloud detectability from satellite and its impact on attempts at TOA CRF closure.

Erica K. Dolinar↗
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 NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗
NASA NTRS2019

Investigation of CATS Aerosol Products and Application Toward Global Diurnal Variation of Aerosols

We present a comparison of 1064 nm aerosol optical depth (AOD) and aerosol extinction profiles from the Cloud-Aerosol Transport System (CATS) level 2 aerosol product with collocated Aerosol Robotic Network (AERONET) AOD, Moderate Imaging Spectroradiometer (MODIS) Aqua and Terra Dark Target AOD and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD and extinction data for the period of March 2015–October 2017. Upon quality-assurance checks of CATS data, reasonable agreement is found between aerosol data from CATS and other sensors. Using quality-assured CATS aerosol data, for the first time, variations in AODs and aerosol extinction profiles are evaluated at 00:00, 06:00, 12:00 and 18:00 UTC (and/or 00:00, 06:00, 12:00 and 18:00 local time or LT) on both regional and global scales. This study suggests that marginal variations are found in AOD from a global mean perspective, with the minimum aerosol extinction values found at 18:00 LT near the surface layer for global oceans, for both the June–November and December–May seasons. Over land, below 500 m, the daily minimum and maximum aerosol extinction values are found at 12:00 and 00:00/06:00 LT, respectively. Strong diurnal variations are also found over north Africa, the Middle East and India for the December–May season, and over north Africa, south Africa, the Middle East and India for the June–November season.

Logan Lee↗
NASA NTRSDate not supplied

The Atmosphere Observing System (AOS): Synergistic Aerosol, Cloud, Convection and Precipitation Measurement and Modeling Systems

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs). On June 1, 2018, several NASA centers (GSFC, LaRC, JPL, MSFC, GRC and ARC) submitted a joint Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (ACCP), with the ACCP Study concluding in early 2021. The new mission now in pre-phase A is being referred to as the Atmosphere Observing System (AOS), an integral part of NASA’s Earth System Observatory (ESO) strategy. The DS and the ACCP team recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of science objectives and also to provide an expanded capability to address additional objectives beyond those of the individual DOs. A critical element of the ACCP observing strategy is to make extensive use of new passive and active sensors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires comprehensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, atmospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data for advancing operational predictions and to generate expanded hindcasts and reconstruction of the climate record.

Arlindo da Silva↗
NASA NTRSDate not supplied

The ACCP Inclined Orbit Project: Enabling New and Synergistic Aerosol Remote Sensing Inversions

Atmospheric aerosols have high spatiotemporal variations due to their diverse sources including windblown dust, wildfires, volcanic eruptions, and anthropogenic emissions. These aerosols also have strong diurnal emission characteristics that influence long-range transport, air quality, Earth’s radiation budget, biogeochemical cycles, and weather forecasting. While current sensors can measure the steady-state of global 3D cloud and aerosol properties, we lack the observational datasets to discover the diurnal variability of aerosols, even at seasonal and regional scales, because these missions utilize sun synchronous orbits. An Aerosols and Clouds, Convection and Precipitation (ACCP) observing system has been established as part of NASA’s new Earth System Observatory (ESO) to fulfill the science needs presented in the 2017 Earth Science Decadal Survey. The selected ACCP architecture encompasses sensors in two orbit planes, one of which utilizes an inclined orbit (nominally ~55 degrees at a 407 km altitude) to advance our understanding of aerosol and cloud properties and target the dynamics of the cloud and aerosol processes on sub-daily time scales. The anticipated instrumentation suite for the ACCP Inclined Orbit Project is a Stereo Camera System, Polarimeter, Microwave Radiometer, Two-Frequency Radar, and Backscatter Lidar. The polarimeter is expected to have multiple wavelength bands and viewing angles to enable accurate retrievals of aerosol microphysical and optical properties. The backscatter lidar will provide vertical profiles of attenuated backscatter and depolarization ratio at two wavelengths, with better daytime signal-to-noise ratio than previous space-based lidar systems to improve aerosol detection/typing and estimates of aerosol optical properties. The stereo camera system, while designed primarily for clouds, will also provide aerosol plume heights and air motions at the plume heights. This invited presentation will provide (1) an overview of the ACCP Inclined Orbit Project, (2) a description of the new sensor capabilities and orbital characteristics, and (3) a preview of the potential for new synergistic aerosol inversion techniques.

John E Yorks↗
NASA NTRSDate not supplied

Generation of Merged Radar-Lidar Data Products during the IMPACTS 2020 Field Campaign

A novel, composite lidar and radar data products was created using lidar and radar data products generated from the NASA ER-2 aircraft during the NASA IMPACTS 2020 field campaign. This product is intended to supplement the raw data sources, tease out additional aspects of the mechanisms underpinning wintertime cyclones, and enhance our understanding of microphysical properties in sensor overlap regions. Initial combined products focused on generating normalized fields of ER-2 based radar and lidar data to provide a more comprehensive visualization of storm structure (precipitation bands, cloud tops, melting levels, etc.) Ongoing work has focused on developing a combined radar-lidar reflectivity product and deriving hydrometeor particle properties (type, orientation, etc.) using ER-2-based depolarization data. To validate these hydrometeor properties, we leverage coincident overpasses of the high-altitude ER-2 aircraft with the suite of cloud particle probes on the in-situ P-3 aircraft during coincident flight overpasses. This work aims to provide vital information to help IMPACTS achieve its mission goal of improving microphysical properties retrievals from airborne and spaceborne platforms for these high-impact winter storm events.

Stephen D Nicholls↗
NASA NTRSDate not supplied

The Roscoe Lidar: Initial Results from the Upward and Downward Looking Airborne Lidar from the ACCLIP and SABRE Campaigns

Backscatter lidars are a staple of airborne remote sensing and are primarily used in determining the altitude, vertical structure, and optical properties of clouds and aerosols. These airborne lidars typically observe in a downward direction which is sufficient for most tropospheric objectives. With more scientific attention being directed to upper tropospheric/lower stratospheric (UTLS) aerosols and recent volcanic and phrocumulonimbus events in the news, developing lidar instruments that can observe the UTLS will be vital in better understanding their composition and optical properties and determining their role in the planet's radiative balance. To meet this need, the downward and upward looking Roscoe lidar was developed and flown on two recent field campaigns: Stratospheric Aerosol processes, Budget, and Radiative Effects (SABRE) and Asian Summer Monsoon Chemical and CLimate Impact Project (ACCLIP) (both 2022). Building on its extensive Cloud Physics Lidar (CPL) heritage, we show that Roscoe can deliver CPL-like products in both the up and down-looking directions providing an unmatched vertical view of the atmosphere from an airborne platform. Future work can leverage Roscoe's updward looking view to quantify the optical properties and extinction of UTLS aerosols.

Kenneth Christian↗
NASA NTRSDate not supplied

Autoencoders for Denoising Atmospheric Profiles from ICESat-2

Abstract: The 2nd generation Ice, Cloud, and land Elevation Satellite (ICESat-2) is an altimetry mission designed primarily for measuring ice sheet elevation and sea ice thickness, provides atmospheric profiles of clouds and aerosols at 532 nm using a photo counting detection approach. While highly sensitive for the detection of tenuous aerosol and cloud features, during the day signal-to-noise-ratio (SNR) photon counting detectors are adversely impacted by solar contributions to the total signal. Averaging the data to coarser horizontal resolutions has been the standard way to increase SNR and thus allow clouds and aerosols to be more easily detectable. Recent work has demonstrated success in boosting SNR without decreasing resolution using advanced filtering techniques [Yorks et al., 2021], however, rapid advancements in Deep Learning based image denoising algorithms can further improve the SNR. Here, we present results using a state-of-the-art Deep Learning autoencoder applied to noisy ICESat-2 data to improve daytime SNR and discuss implications for atmospheric feature detection, classification, and optical property retrievals.

denoising↗