Results for “detection cloud classification algorithm” — Page 2

NASA NTRS2005

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗

NASA NTRS2017

Imaging Systems for Size Measurements of Debrisat Fragments

The overall objective of the DebriSat project is to provide data to update existing standard spacecraft breakup models. One of the key sets of parameters used in these models is the physical dimensions of the fragments (i.e., length, average-cross sectional area, and volume). For the DebriSat project, only fragments with at least one dimension greater than 2 mm are collected and processed. Additionally, a significant portion of the fragments recovered from the impact test are needle-like and/or flat plate-like fragments where their heights are almost negligible in comparison to their other dimensions. As a result, two fragment size categories were defined: 2D objects and 3D objects. While measurement systems are commercially available, factors such as measurement rates, system adaptability, size characterization limitations and equipment costs presented significant challenges to the project and a decision was made to develop our own size characterization systems. The size characterization systems consist of two automated image systems, one referred to as the 3D imaging system and the other as the 2D imaging system. Which imaging system to use depends on the classification of the fragment being measured. Both imaging systems utilize point-and-shoot cameras for object image acquisition and create representative point clouds of the fragments. The 3D imaging system utilizes a space-carving algorithm to generate a 3D point cloud, while the 2D imaging system utilizes an edge detection algorithm to generate a 2D point cloud. From the point clouds, the three largest orthogonal dimensions are determined using a convex hull algorithm. For 3D objects, in addition to the three largest orthogonal dimensions, the volume is computed via an alpha-shape algorithm applied to the point clouds. The average cross-sectional area is also computed for 3D objects. Both imaging systems have automated size measurements (image acquisition and image processing) driven by the need to quickly and accurately measure tens of thousands of debris fragments. Moreover, the automated size measurement reduces potential fragment damage/mishandling and ability for accuracy and repeatability. As the fragment characterization progressed, it became evident that the imaging systems had to be revised. For example, an additional view was added to the 2D imaging system to capture the height of the 2D object. This paper presents the DebriSat project's imaging systems and calculation techniques in detail; from design and development to maturation. The experiences and challenges are also shared.

Shiotani, B.↗

DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

CALIPSO Lidar Level 3 Aerosol Profile Product: Version 3 Algorithm Design

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The “extinction QC flag” filter is the most influential in preventing high-biases in level 3 mean extinction, while the “misclassified cirrus fringe” filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by −24 and −31 % for global ocean and global land, respectively, indicating the importance of quality screening.

Tackett, Jason L.↗

NASA NTRS2021

Improving the CERES SYN Cloud and Flux Products by Identifying GOES-17 Scan Anomalies Using a Convolutional Neural Network

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

Benjamin Scarino↗

NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗

NASA NTRS2019

Image Labeler: A Web Interface to Catalog Earth Science Events

Advances in machine learning (ML) have made it possible to automatically detect Earth science phenomena from satellite imagery. While useful, ML algorithms typically require an extensive dataset containing labeled images for training. Systematic labeling and management of such datasets is quite cumbersome. With this in mind, we present the Image Labeler. Image Labeler is a fast and scalable cloud-based tool that facilitates the rapid development of Earth science event databases, in order to aid automated ML-based image classification.

Case Study↗

NASA NTRS2008

FPGA Coprocessor for Accelerated Classification of Images

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

Pingree, Paula J.↗

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↗

NASA NTRS2012

An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces.

Herzfeld, Ute C.↗

NASA NTRS2019

2018 NISAR Applications Workshop: Forest and Disturbance; Workshop Report

Forest lands cover the globe and are important sources for providing ecosystem services including: carbon sequestration, biodiversity, timber, air and water quality. As such, counties around the world have dedicated programs for managing them. Accurate and timely information concerning the status of these forests (moisture, biomass, disturbance type, etc.) is essential to those Nations’ human and ecological health as well as economy. The joint NASA/US Forest Service workshop focused on arming forest land managers with observations and remote sensing information from the upcoming NASA-ISRO (Indian Space Research Organization) SAR (Synthetic Aperture Radar) (NISAR) satellite mission (expected to launch early 2022). Participants included representatives from different US Federal Agencies, private sector, and non-governmental organizations (NGO) that are key players in facilitating integration of Earth Observations (EO) into forest management and decision support workflows. They included scientists, technicians, and program managers with a responsibility for data acquisition and exploitation such as product development, delivery, and use, and capacity building. Discussions were held over two days to convey the broader forest and disturbance community information needs for various representative participants and programs and to facilitate the delivery of NISAR mission geospatial products and observational capabilities. Case studies were presented to demonstrate the current state of practice in the use of SAR remote sensing for applications of direct importance to forest and disturbance land management community. Eleven organizations presented their information requirements in response to a set of questions provided by the NASA team, then the NASA team responded by describing the degree to which NISAR could meet these requirements. Discussion ensued about needed data product specifications to increase utility (e.g., projection, latency, etc.), tools and capacity building. The general findings of this workshop were that (a) NISAR observations will be particularly useful to the global forest carbon and disturbance monitoring applications, but that certain data product design decisions (projections and radiometric and terrain corrections) need to be considered to increase utility; b) the biomass and disturbance detection algorithms meet many of the community needs, however there are other information products of value (e.g., soil moisture or disturbance classification, not just detection) and all products should be compliant with existing community standards for reporting uncertainty; c) providing SAR education to the community will be key specifically thinking about putting the information first and the SAR theory second, providing a simple guide of standard data processing steps (e.g., dB (decibel) to power conversion and speckle filtering); d) the community needs a user-friendly interface for finding free, archived data over their geographic regions of interest; e) user-friendly tools that connect to open-sources GIS (Global Information System) software (e.g., QGIS (Quantum GIS)) that include a graphical user interface (GUI) for SAR processing that enables both download and cloud processing. To integrate these findings and prepare the community before NISAR launches, it was suggested that there be a dedicated NISAR Forest and Disturbance Applications Working Group (as per the specifications in the NISAR Utilization Plan). After launch, it was decided that the community continue capacity building activities.

Stavros, Natasha↗

NASA NTRS2018

(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidarâ€“radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

Lamer, K.↗

NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗

NASA NTRS2015

9+ Years of CALIPSO PSC Observations: An Evolving Climatology

Polar stratospheric clouds (PSCs) play a crucial role in the springtime chemical depletion of ozone at high latitudes. PSC particles (primarily supercooled ternary solution, or STS droplets) provide sites for heterogeneous chemical reactions that transform stable chlorine and bromine reservoir species into highly reactive ozone-destructive forms. Furthermore, large nitric acid trihydrate (NAT) PSC particles can irreversibly redistribute odd nitrogen through gravitational sedimentation (a process commonly known as denitrification), which prolongs the ozone depletion process by slowing the reformation of the stable chlorine reservoirs. Spaceborne observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite are providing a rich new dataset for studying PSCs. CALIPSO is an excellent platform for studying polar processes with CALIOP acquiring, on average, over 300,000 backscatter profiles daily at latitudes between 55o and 82o in both hemispheres. PSCs are detected in the CALIOP backscatter profiles using a successive horizontal averaging scheme that enables detection of strongly scattering PSCs (e.g., ice) at the finest possible spatial resolution (5 km), while enhancing the detection of very tenuous PSCs (e.g., low number density NAT) at larger spatial scales (up to 135 km). CALIOP PSCs are separated into composition classes (STS; liquid/NAT mixtures; and ice) based on the ensemble 532-nm scattering ratio (the ratio of total-to-molecular backscatter) and 532-nm particulate depolarization ratio (which is sensitive to the presence of non-spherical, i.e. NAT and ice particles). In this paper, we will provide an overview of the CALIOP PSC detection and composition classification algorithm and then examine the vertical and spatial distribution of PSCs in the Arctic and Antarctic on vortex-wide scales for entire PSC seasons over the more than nine-year data record from 2006- 2015.

Pitts, Michael C.↗

NASA NTRS2015

CALIPSO Polar Stratospheric Cloud Observations from 2006-2015

Polar stratospheric clouds (PSCs) play a crucial role in the springtime chemical depletion of ozone at high latitudes. PSC particles (primarily supercooled ternary solution, or STS droplets) provide sites for heterogeneous chemical reactions that transform stable chlorine and bromine reservoir species into highly reactive ozone-destructive forms. Furthermore, large nitric acid trihydrate (NAT) PSC particles can irreversibly redistribute odd nitrogen through gravitational sedimentation (a process commonly known as denitrification), which prolongs the ozone depletion process by slowing the reformation of the stable chlorine reservoirs. Spaceborne observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite are providing a rich new dataset for studying PSCs. CALIPSO is an excellent platform for studying polar processes with CALIOP acquiring, on average, over 300,000 backscatter profiles daily at latitudes between 55o and 82o in both hemispheres. PSCs are detected in the CALIOP backscatter profiles using a successive horizontal averaging scheme that enables detection of strongly scattering PSCs (e.g., ice) at the finest possible spatial resolution (5 km), while enhancing the detection of very tenuous PSCs (e.g., low number density NAT) at larger spatial scales (up to 135 km). CALIOP PSCs are separated into composition classes (STS; liquid/NAT mixtures; and ice) based on the ensemble 532-nm scattering ratio (the ratio of total-to-molecular backscatter) and 532-nm particulate depolarization ratio (which is sensitive to the presence of non-spherical, i.e. NAT and ice particles). In this paper, we will provide an overview of the CALIOP PSC detection and composition classification algorithm and then examine the vertical and spatial distribution of PSCs in the Arctic and Antarctic on vortex-wide scales for entire PSC seasons over the more than nine-year data record from 2006- 2015.

Pitts, Michael C.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-20076-DND

Polar stratospheric clouds (PSCs) play a crucial role in the springtime chemical depletion of ozone at high latitudes. PSC particles (primarily supercooled ternary solution, or STS droplets) provide sites for heterogeneous chemical reactions that transform stable chlorine and bromine reservoir species into highly reactive ozone-destructive forms. Furthermore, large nitric acid trihydrate (NAT) PSC particles can irreversibly redistribute odd nitrogen through gravitational sedimentation (a process commonly known as denitrification), which prolongs the ozone depletion process by slowing the reformation of the stable chlorine reservoirs. Spaceborne observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite are providing a rich new dataset for studying PSCs. CALIPSO is an excellent platform for studying polar processes with CALIOP acquiring, on average, over 300,000 backscatter profiles daily at latitudes between 55o and 82o in both hemispheres. PSCs are detected in the CALIOP backscatter profiles using a successive horizontal averaging scheme that enables detection of strongly scattering PSCs (e.g., ice) at the finest possible spatial resolution (5 km), while enhancing the detection of very tenuous PSCs (e.g., low number density NAT) at larger spatial scales (up to 135 km). CALIOP PSCs are separated into composition classes (STS; liquid/NAT mixtures; and ice) based on the ensemble 532-nm scattering ratio (the ratio of total-to-molecular backscatter) and 532-nm particulate depolarization ratio (which is sensitive to the presence of non-spherical, i.e. NAT and ice particles). In this paper, we will provide an overview of the CALIOP PSC detection and composition classification algorithm and then examine the vertical and spatial distribution of PSCs in the Arctic and Antarctic on vortex-wide scales for entire PSC seasons over the more than eight-year data record.

Michael C Pitts↗

NASA NTRSDate not supplied

A Synopsis of the 8+ Year CALIOP Polar Stratospheric Cloud Data Record

Polar stratospheric clouds (PSCs) play a crucial role in the springtime chemical depletion of ozone at high latitudes. PSC particles (primarily supercooled ternary solution, or STS droplets) provide sites for heterogeneous chemical reactions that transform stable chlorine and bromine reservoir species into highly reactive ozone-destructive forms. Furthermore, large nitric acid trihydrate (NAT) PSC particles can irreversibly redistribute odd nitrogen through gravitational sedimentation (a process commonly known as denitrification), which prolongs the ozone depletion process by slowing the reformation of the stable chlorine reservoirs. Spaceborne observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite are providing a rich new dataset for studying PSCs. CALIPSO is an excellent platform for studying polar processes with CALIOP acquiring, on average, over 300,000 backscatter profiles daily at latitudes between 55o and 82o in both hemispheres. PSCs are detected in the CALIOP backscatter profiles using a successive horizontal averaging scheme that enables detection of strongly scattering PSCs (e.g., ice) at the finest possible spatial resolution (5 km), while enhancing the detection of very tenuous PSCs (e.g., low number density NAT) at larger spatial scales (up to 135 km). CALIOP PSCs are separated into composition classes (STS; liquid/NAT mixtures; and ice) based on the ensemble 532-nm scattering ratio (the ratio of total-to-molecular backscatter) and 532-nm particulate depolarization ratio (which is sensitive to the presence of non-spherical, i.e. NAT and ice particles). In this paper, we will provide an overview of the CALIOP PSC detection and composition classification algorithm and then examine the vertical and spatial distribution of PSCs in the Arctic and Antarctic on vortex-wide scales for entire PSC seasons over the more than eight-year data record.

Michael C Pitts↗

NASA NTRS1992

Effects of weather on the retrieval of sea ice concentration and ice type from passive microwave data

Effects of wind, water vapor, and cloud liquid water on ice concentration and ice type calculated from passive microwave data are assessed through radiative transfer calculations and observations. These weather effects can cause overestimates in ice concentration and more substantial underestimates in multi-year ice percentage by decreasing polarization and by decreasing the gradient between frequencies. The effect of surface temperature and air temperature on the magnitudes of weather-related errors is small for ice concentration and substantial for multiyear ice percentage. The existing weather filter in the NASA Team Algorithm addresses only weather effects over open ocean; the additional use of local open-ocean tie points and an alternative weather correction for the marginal ice zone can further reduce errors due to weather. Ice concentrations calculated using 37 versus 18 GHz data show little difference in total ice covered area, but greater differences in intermediate concentration classes. Given the magnitude of weather-related errors in ice classification from passive microwave data, corrections for weather effects may be necessary to detect small trends in ice covered area and ice type for climate studies.

Maslanik, J. A.↗