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At least 307 records · Page 17

Keeping Up With the Time(s): Advancing NASA’s Worldview to Facilitate Quick Access to Global Near Real-Time Imagery

Striving to keep up with the constantly changing environment, whether physical or computing, NASA Worldview’s interactive web mapping application continues to make improvements to ensure that avenues to near-real time imagery are quick, easy and accessible. This poster will explore recent additions to NASA Worldview and the Global Imagery Browse Services (GIBS) of near real-time imagery and imagery visualizations - from geostationary to flood mapping visualizations; improvements to access to imagery - from granule swath visualizations to vector interactions with fires and other types of vectors; and improvements to imagery discovery - from enhancements to the timeline to providing more ways to locate relevant imagery in the layer picker.

Min Minnie Wong↗

Southern Idaho Health & Air Quality: Monitoring Atmospheric Mixing Heights Post-Wildfire Through the Use of NASA Earth Observations

Wildfire smoke has long-lasting impacts on public and environmental health. Currently, agencies that monitor smoke base their decisions on an analysis of how fires burn, the direction the smoke moves from the fire source, and unverified estimates of mixing height. Mixing heights describe the maximum altitude to which a smoke plume rises. Satellite imagery provides more continuous and accurate coverage of mixing heights than current in situ methods. Thus, the team developed a software tool that processes and extracts mixing height observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask granules. The team partnered with the National Oceanic and Atmospheric Administration’s National Weather Service, the Bureau of Land Management’s National Interagency Fire Center, and the National Park Service’s Fire Management Program Center to analyze historic fire events in southern Idaho. To do so, they used Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) to verify where a CALIPSO pass intersects thermal anomalies and smoke plumes. The software extracts features of relevance from the hdf file of each CALIPSO transect to locate layers of continuous aerosols. The maximum altitude at which the aerosol ends is recorded as the mixing height, along with a matching latitude and longitude. The satellite-derived values can be used to validate past mixing height predictions and evaluate the accuracy and systematic bias of different estimation methods. These results may allow agencies to make better comparisons and subsequent smoke pollution management, prevention, and public health decisions if the spatial and temporal differences between predictions and observations can be resolved.

DEVELOP Project Summary↗

Latest Progress and Results in LEO-GEO and GEO-GEO Stereo AMVs

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Atmospheric motion vectors↗

Effects of Rotation on Internal Structure and Dynamics of Main-Sequence Stars

Current state-of-the-art computer simulations allow us to build 3D dynamical and radiative models of stars from physical first principles with a high degree of realism. The radiative 3D dynamical stellar models obtained with the StellarBox code take into account the effects of turbulence, stellar abundances, a realistic equation of state, and radiative energy transport. In this talk, I will discuss the effects of rotation on the turbulent dynamics and surface structure for a 1.47Msun star for rotational periods of 1 and 14 days. The simulations are performed with the computational domain at various latitudes. The models reproduce stellar granulation, the subsurface shear layer, structural changes in convection, and the tachocline, which is the interface between the inner radiative zone and the outer convection zone and plays a crucial role in stellar variability. In particular, the model results reveal the formation of differential rotation and meridional circulation.

SMD↗

Amino Acid Abundances and Compositions in Iron and Stony-Iron Meteorites

The organic compositions of carbonaceous chondrite meteorites have been extensively studied; however, there have been fewer reports of other meteorite classes, and almost none from iron meteorites, which contain much less carbon than carbonaceous chondrites but make up ~4% of observed meteorite falls. Here, we report the bulk amino acid content of three iron meteorites (Campo del Cielo, IAB; Canyon Diablo, IAB; and Cape York, IIIAB) and both the metal and silicate portions of a pallasite (Imilac). We developed a novel method to prepare the samples for analysis, followed by hot water extraction and analysis via liquid chromatography‐mass spectrometry. Free amino acid abundances ranging from 301 to 1216 pmol/g were observed in the meteorites, with the highest abundance in the silicate portion of the pallasite. Although some of the amino acid content could be attributed to terrestrial contamination, evidence suggests that some compounds are indigenous. A suite of C(5) amino acids was observed with a distinct distribution favoring a straight chain (n‐pentanoic acid) structure; this straight chain dominance is suggestive of that observed in thermally altered stony meteorites. Amino acids were also observed in terrestrial iron granules that were milled and analyzed in the same way as the meteorites, although the distribution of detected amino acids was different. It is possible that similar formation mechanisms existed in both the meteorites and the terrestrial iron, or that observed amino acids resulted from reactions of precursors during sample preparation. This work suggests that iron meteorites should not be overlooked for contributions of amino acids and likely other soluble organic molecules to the early Earth. Future studies of iron–nickel meteorites and asteroids, such as Psyche, may provide further insights into their potential organic inventory.

Jamie E. Elsila↗

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark‐Target Algorithm: 1. Dust Detection

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark-Target (DT) algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near-UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET-MODIS data set and collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-MODIS data set. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1–2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with aerosol optical depth (AOD) and Angstrom exponent (AE), the new dust detection algorithm finds more dusty pixels and fewer false detections.

spectral dust detection↗

Southern Idaho Health & Air Quality II: Evaluating Atmospheric Mixing Height Estimations in the Western United States

Wildfires in the western United States have caused immense infrastructure damage and loss of human life in recent years. Wildfire smoke, which travels far from its original source, is also harmful to human health. Mixing height, which acts as a lid and prevents smoke from rising above a certain altitude in the lower troposphere, is a critical input in smoke dispersion and air quality models used by agencies that monitor wildfires. These models, coupled with forecaster expertise, are also used to decide when it is safe to execute a prescribed burn. The DEVELOPID team partnered with the National Weather Service (NWS) Fire Weather Program, Bureau of Land Management (BLM), National Interagency Fire Center (NIFC), and National Park Service (NPS) Fire Management Program Center (FMPC) to help improve reliability and confidence in mixing height estimations, and therefore the burn prescription decision-making process. To that end, the team developed a toolbox for measuring smoke-related aerosol mixing heights using Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask granules. CALIPSO mixing heights and NWS estimations covaried significantly and positively. However, substantial disagreement between the methods stymied the team’s attempts to quantify systematic bias in a meaningful way. The relative error between the methods was especially large at low mixing heights, which suggests that this method of validation may only be suitable at higher altitudes.

Dean Berkowitz↗

Exploring rock-regolith interfaces in Jezero crater with Mars 2020 SHERLOC

The Perseverance rover successfully landed in Jezero crater, Mars in February 2021 at the Octavia E. Butler landing site and began its mission to explore and sample an ancient crater lake basin. Principal goals of the Mars 2020 mission include characterizing the geology of Mars and seeking signs of ancient microbial life via the spacecraft cameras and spectroscopic instruments onboard. The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument is a deep UV Raman spectrometer that utilizes a 248.6nm pulsed laser. Part of SHERLOC is a color camera known as the Wide Angle Topographic Sensor for Operations and eNgineering (WATSON). The SHERLOC suite provides coordinated, spectroscopic and imaging capabilities at high spatial resolution, to detect minerals and organic molecules in microtextural context. By pairing high spatial resolution (~100 μm) resonance Raman and native fluorescence spectroscopy with microscopic imaging in a novel spacecraft capability, SHERLOC enables texture-specific molecular composition measurements of rock and regolith targets on Mars. Coordinated rock-regolith observations illuminate unique insights into weathering processes and thereby to primary properties of rocks in Jezero crater. This work describes the potential of rock-regolith interfaces to preserve unique records of geological processes in Jezero crater and can powerfully supplement observations of the more general rock record on Mars. Linking observations of local rock texture with associated regolith reveals important lithologic information based on the interrelationship between differential weathering behavior and mineralogy, grain size, and cement chemistry. Preliminary observations indicate that the polygonally fractured lithotype common near the Octavia E. Butler landing site may weather by granular disintegration and/or surface creep, a relation that can be uniquely observed at the rock-regolith interface. SHERLOC -specific observations of microtextural and elemental composition transitions presented here trace rock-regolith boundaries at multiple indurated surfaces adjacent to regolith. At these locales, grain-scale based examinations suggest chemical weathering could be related to a variably distributed coating or rind on dark rock targets that may be mafic in composition. Granule deposits overlying widely distributed fine-grained material are also observable. Mineral identifications of each phase are presented, with cross-scale comparisons to the remote insights gained by the SuperCam instrument.

Emily L. Cardarelli↗

Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

EPIC↗

pyQuARC: Open Source Library for Earth Observation Metadata Quality Assessment

Metadata quality is essential to effective data discovery and has become increasingly vital as more Earth Science data sets become available. The Common Metadata Repository (CMR) hosts metadata describing NASA’s Earth Observation data products, which are archived across 12 Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, conducts metadata quality assessments to ensure that these data products are discoverable, accessible, and usable. To achieve these goals, the ARC team has developed a metadata quality assessment framework to evaluate metadata completeness, correctness, and consistency. ARC uses a combination of manual and automated methods to assess these three components and identify areas of improvement; the team then collaborates with the DAACs to resolve any findings. To streamline this process, ARC is currently developing a host of scripts, known as pyQuARC, to automate metadata quality assessments as much as possible. pyQuARC is an open source library for Earth Observation Metadata Quality Assessment, and the tool utilizes ARC’s metadata quality assessment framework to make basic validation checks, pinpoint inconsistencies between dataset-level (i.e. collection) and file-level (i.e. granule) metadata, and identify opportunities for more descriptive and robust information. Since pyQuARC is also customizable, other users can make modifications as needed, and future metadata standards can also be implemented. Once pyQuARC is fully developed, it will support multiple schema types to serve the broader EOSDIS metadata community. This presentation will provide an overview of pyQuARC and its process of development while showcasing the tool’s valuable features and uses.

Jenny Wood↗

Comparison of the CALIPSO Level 3 Ice Cloud Product with the DARDAR and 2C-ICE products

Understanding the vertical distribution of ice clouds is crucial for climate modeling and weather forecasting. Since its launch to space in 2006, the Cloud-Aerosol Lidar with Orthogonal Polarization instrument (CALIOP) onboard the CALIPSO spacecraft has been providing unprecedented high-quality profiles of ice clouds, especially optically thin cirrus clouds, during both day and night, on a global scale. Recently the lidar science working group at the NASA Langley Research Center delivered a level 3 (L3) ice cloud product which reports monthly statistics of ice cloud extinction coefficient and ice water content on a uniform 3-dimensional spatial grid. This presentation compares the ice cloud climatology derived from this product with two other ice cloud products, namely the raDAR/liDAR(DARDAR) ice cloud product and the CloudSat-CALIPSO Ice Cloud Property Product (2C-ICE). Using processing similar to the CALIPSO L3 product, granules of DARDAR and 2C-ICE data have been processed into monthly statistics for a better comparison. Similarities and differences will be presented and discussed. The findings illustrate the strengths of the different products and help to identify which product might be most appropriate for a given research topic.

Xia Cai↗

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products from MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03),central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals thanMOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereasMOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

MAIAC↗

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm’s ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies n an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30%, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within +/-20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five 38 used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset -0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40%, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

MODIS↗

The membrane bioreactor (MBR): A hybrid technology for bioregenerative wastewater treatment and resource recovery in space

Extraplanetary surface habitat life support systems (LSS) on the Moon and Mars, as well as long-duration space travel, will require novel capabilities to withstand anticipated unique, harsh conditions. In order to provide safe, habitable environments for the crew, water purification and waste processing systems will be required to treat all sources of water (condensate, Sabatier, urine, hygiene, fecal, food waste) in order to achieve the necessary levels of recovery needed to sustain life over the long-duration missions. The ability to recycle organic wastes creates an opportunity to recover critical elements (e.g., C, H, O, N, P) for subsequent food production, water purification, and atmospheric regeneration. Bioregenerative systems mimic functions of nature in engineered systems, or bioreactors, utilizing combination of prokaryotes, eukaryotes and archaea. While these systems are commonly used on Earth for wastewater treatment, bioreactors for space travel face additional challenges. Terrestrial bioreactors often rely on gravitational settling of dense flocs and granules for cell retention. For micro- or partial-gravity environments, density differential alone will not be adequate for cell retention; a gravity-independent means for cell retention is crucial. The membrane bioreactor represents the state of the art in wastewater treatment. This hybrid system combines biological processes with membrane filtration to achieve performance beyond what each can accomplish individually. The complete cell retention in an MBR allows for the decoupling of hydraulic retention time (HRT) and solids retention time (SRT), which result in a high-thruput, compact, treatment system. The Bioregenerative Water Technology Team at NASA Kennedy Space Center and the University of South Florida has developed a bioregenerative platform based on the hybrid MBR technology. The overall architecture is compact, modular, flexible, and adaptable to mission evolutions. The main subsystems of the bioregenerative architecture include: 1) Anaerobic membrane bioreactor (AnMBR): Also termed the Organic processor assembly (OPA), the function of the AnMBR is to treat organic wastes such as fecal and food wastes. These wastes are characterized by a concentration of suspended solids comprised of carbohydrates, proteins and lipids. The assigned function of the AnMBR is to break down and covert suspended solids to biogas (methane, hydrogen and carbon dioxide), reduce effluent chemical oxygen demand (COD), liberate organically-bound nutrients, and remove pathogenic organisms. 2) Phototrophic membrane bioreactor (PMBR): The PMBR is comprised of a co-culture of microalgae and bacteria. The assigned function of the PMBR is to polish the permeate of the AnMBR to further remove dissolved organic carbon, manage nutrients (nitrogen transformation, load dampening), and perform air revitalization. 3) Food processor assembly (FPA): The FPA is a food production platform (prokaryotic or eukaryotic), fueled by outputs from the AnMBR, or PMBR. For the presentation, we will describe each step of the bioregenerative architecture, and present performance data from extended trials treating analog and real metabolic wastes.

Bioreactor↗

Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network

In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in Level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.

Adam B Milstein↗

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, are used to estimate the broadband fluxes between CERES observations. This requires stable and consistent cross-platform imager visible channel calibration. The CERES project utilizes deep convective clouds (DCC) as an invariant Earth target to both monitor the stability of sensors and for radiometric scaling. GSICS, an international collaboration, is also evaluating and implementing the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the AquaMODIS or the NOAA-20 VIIRS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical D CC-identified pixel-level reflectances, which are aggregated as probability density functions (PDF). By assuming the monthly PDF shape is otherwise consistent in time excepting shifts in reflectance caused by changes in the sensor calibration, the imager stability is monitored. Radiometric scaling is accomplished by ratioing the sensor pair DCC PDF reflectance values. The success of the DCC methodology relies on consistent PDF distributions. The goal of this study is to determine the impact of pixel resolution on the DCC reflectance distribution. Single SNPP-VIIRS 750-m and Landsat 8 OLI 30-m granules are aggregated to degrade the pixel resolution from the native level. The DCC pixels are identified using a BT threshold. Most of the brightest DCC pixels are also the coldest, although there are exceptions. It was found that increasing the BT threshold exponentially increased the number of darker pixels. The pixel resolution did not seem to impact the DCC reflectance PDF distribution for pixel resolutions less than 3 km, which suggests that imagers of varying pixel resolutions may be radiometrically scaled to each other using DCC targets.

DCC↗

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗