ALBERT

Description: The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates.

Description: These maps are an analysis of the Thomas Fire that occurred in California during December 2017. Using a variety of NASA Earth science data from five National Aeronautics and Space Administration (NASA) sources (including four Earth Observing System Data and Information System Distributed Active Archive Centers and NASA Fire Information for Resource Management System), as well as ancillary data from Ventura County, Santa Barbara County, and the Department of Homeland Security, this analysis sought to identify forest fire risk zones, create a fire occurrence density map, examine the vegetation and subsequent burn scar, capture the affected parcels, and capture the affected vegetation.

Description: An extreme biomass-burning event occurred in Indonesia from September through October 2015 due to severe drought conditions, partially caused by a major El Nino event, thereby allowing for significant burning of peatland that had been previously drained. This event had the highest sustained aerosol optical depths (AOD) ever monitored by the global Aerosol Robotic Network (AERONET). The newly developed AERONET Version 3 algorithms retain high AOD at the longer wavelengths when associated with high Angstrom Exponents (AEs), which thereby allowed for measurements of AOD at 675 nanometers as high as approximately 7, the upper limit of Sun photometry. Measured AEs at the highest monitored AOD levels were subsequently utilized to estimate instantaneous values of AOD at 550 nanometers in the range of 11 to 13, well beyond the upper measurement limit. Additionally, retrievals of complex refractive indices, size distributions, and single scattering albedos (SSA) were obtained at much higher AOD levels than possible from almucantar scans due to the ability to perform retrievals at smaller solar zenith angles with new hybrid sky radiance scans. For retrievals made at the highest AOD levels the fine mode volume median radii were approximately 0.25 to 0.30 microns, which are very large particles for biomass burning. Very high SSA values (approximately 0.975 from 440 to 1020 nanometers) are consistent with the domination by smoldering combustion of peat burning. Estimates of the percentage peat contribution to total biomass burning aerosol based on retrieved SSA and laboratory measured peat SSA were approximately 80-85 percent, in excellent agreement with independent estimates.

Description: This analysis is a follow-on to the Thomas Fire analysis presented by Ross Bagwell ("Fire Analysis of the Thomas Fire Using NASA DATA in a GIS"). The Thomas fire and heavy rains a month later led to the historic flooding. The maps tell the story using NASA Earth Observing System data in concert with Santa Barbara County data.

Description: Cloud droplet number concentration (Nd) is an important parameter of liquid clouds and is crucial to understanding aerosol-cloud interactions. It couples boundary layer aerosol composition, size and concentration with cloud reflectivity. It affects cloud evolution, precipitation, radiative forcing, global climate and, through observation, can be used to partially monitor the first indirect effect. With its unique combination of multi-wavelength, multi-angle, total and polarized reflectance measurements, the Research Scanning Polarimeter (RSP) retrieves Nd with relatively few assumptions. The approach involves measuring cloud optical thickness, mean droplet extinction cross-section and cloud physical thickness. Polarimetric observations are capable of measuring the effective variance, or width, of the droplet size distribution. Estimating cloud geometrical thickness is also an important component of the polarimetric Nd retrieval, which is accomplished using polarimetric measurements in a water vapor absorption band to retrieve the amount of in-cloud water vapor and relating this to physical thickness. We highlight the unique abilities and quantify uncertainties of the polarimetric approach. We validate the approach using observational data from the North Atlantic and Marine Ecosystems Study (NAAMES). NAAMES targets specific phases in the seasonal phytoplankton lifecycle and ocean-atmosphere linkages. This study provides an excellent opportunity for the RSP to evaluate its approach of sensing Nd over a range of concentrations and cloud types with in situ measurements from a Cloud Droplet Probe (CDP). The RSP and CDP, along with an array of other instruments, are flown on the NASA C-130 aircraft, which flies in situ and remote sensing legs in sequence. Cloud base heights retrieved by the RSP compare well with those derived in situ (R=0.83) and by a ceilometer aboard the R.V. Atlantis (R=0.79). Comparing geometric mean values from 12 science flights throughout the NAAMES-1 and NAAMES-2 campaigns, we find a strong correlation between Nd retrieved by the RSP and CDP (R=0.96). A linear least squares fit has a slope of 0.92 and an intercept of 0.3 cm3. Uncertainty in this comparison can be attributed to cloud 3D effects, nonlinear liquid water profiles, multilayered clouds, measurement uncertainty, variation in spatial and temporal sampling, and assumptions used within the method. Radiometric uncertainties of the RSP measurements lead to biases on derived optical thickness and cloud physical thickness, but these biases largely cancel out when deriving Nd for most conditions and geometries. We find that a polarimetric approach to sensing Nd is viable and the RSP is capable of accurately retrieving Nd for a variety of cloud types and meteorological conditions.

Description: An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models.

Description: We present a new high-resolution global composition forecast system produced by NASA's Global Modeling and Assimilation Office. The NASA Goddard Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) system combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke.

Description: NASA Earth Science and Aeronautics researchers have been involved in development and use of High Altitude Long Endurance (HALE) unmanned aircraft systems (UAS) since the 1990's. The NASA Environmental Research Aircraft Sensor and Technology Program (ERAST) demonstrated the promise of HALE aircraft for providing observations while also proving the importance of triple-redundant avionics to improve system reliability for large unmanned aircraft. Early efforts to develop an operational HALE capability for earth observations languished for nearly two decades owing to insufficient solar panel efficiency, battery power density, and light-weight, yet strong, materials. During this time NASA researchers focused on using the Global Hawk to demonstrate the utility of providing diurnal measurements over severe storms (ie. HS3) and to track stratospheric water vapor transport (ATTREX). Recent significant commercial investments are now leading to the realization of a long-held goal of week- to month-long sustained observations and measurements from the stratosphere. In addition to a historical review of NASA use and interest in HALE aircraft, this paper will present current concepts for exploiting current and planned HALE aircraft capabilities including in situ characterization of atmospheric composition and dynamics as well as imagery collection. NASA researchers anticipate HALE will provide a useful means to test smallsat instruments and components. Observations from HALE-based instruments might also provide useful gap-filler observations to flagship satellite missions where the repeat time doesn't allow for measurements of quickly changing phenomenon. HALE will likely also provide measurements and communications relay to facilitate other aircraft in multi-aircraft campaigns. We will also report on progress towards a NASA-funded flight test planned for summer 2019 of a solar-electric vehicle designed to carry 7kg (15lbs) for 30 days at 20km altitude.

Description: Nearly all proglacial water discharge from the Greenland Ice Sheet is routed englacially, from the surface to the bed, via moulins. Identification of moulins in high-resolution imagery is a frequent topic of study, but the processes controlling how and where moulins form remain poorly understood. We seek to leverage information gained from the development of a physical model of moulin formation, remotely sensed ice-sheet data products, and an analytic model of ice-flow perturbations to develop a predictive stochastic model of moulin distribution across Greenland. Here we present initial results from the physical model of moulin formation and characterize the sensitivity of moulin geometry to a range of model parameters. This parameterization of moulin formation is the first step in developing a stochastic model that will be a predictive, computationally efficient representation of the englacial hydrologic system.

Description: The NASA PACE project, in conjunction with the IOCCG, EUMETSAT, and JAXA, have initiated an Aquatic Primary Productivity working group, with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 05-07, 2018 at the University Space Research Association headquarters in Columbia, MD U.S.A., bringing together 26 active researchers from 16 institutions. The group discussed the primary differences, nuances, scales, uncertainties, definitions, and best practices for measurements of primary productivity derived from in situ/on-deck/laboratory radio/stable isotope incubations, dissolved oxygen concentrations (from incubations or autonomous platforms such as floats or gliders), oxygen-argon ratios, triple oxygen isotope, natural fluorescence, and FRRF/ETR/kinetic analysis. These discussions highlighted the necessity to move the community forward towards the establishment of climate-quality primary productivity measurements that follow uniform protocols, which is imperative to ensure that existing and future measurements can be compared, assimilated, and their uncertainties determined for model development and validation. The specific deliverable resulting from of this activity will be a protocol document, published in coordination with the IOCCG. This presentation will discuss the findings of the meeting, and address future activities of the working group.

Description: Previous studies have quantified the expansion of gold mining-related forest loss (Espejo et al., 2018; Asner et al., 2017; Swenson et al., 2011) in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map general forest loss within and outside key land tenure areas in this region. Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2018 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

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Description: AVHRR data record is well alive and continue to improve and be used by a large land user community.Most of the improvement are due to the overlapping with MODIS Aqua, Terra.We recommend operating missions as long as possible to enable overlap of at least a few years (especially for applications).

Description: Balloon-borne frostpoint measurements have shown a high frequency of supersaturation near the tropical tropopause, and this has been attributed to forced ascent associated with wavemotions as well as diabatic heating. Long-term profile statistics are typically presented on altitude, pressure or potential temperature surfaces. For example, at Costa Rica long-term mean values of CFH RH at 16.8 km, the mean annual height of the tropopause, range from less than 60 percent in July to over 90% in October. While a plot of the annual cycle vs height shows relatively high humidities in the upper troposphere and especially so as one approaches the tropopause, the overall picture is one of subsaturation. A very different picture emerges,however, if the analysis is done in height relative to the tropopause. Here the long-term average of RH at the tropopause is 94 percent or greater throughout the year. We discuss this paradoxical result in the context of dynamical and cloud processes occurring near the tropical tropopause.

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Description: Stratospheric ozone concentrations have begun to show early signs of recovery following the implementation of the Montreal Protocol and its amendments as well as in response to decreasing upper-stratospheric temperatures. Secular trends in stratospheric ozone are modulated by considerable interannual variability and systematic changes in transport patterns that are expected under increasing concentrations of greenhouse gases, especially in the lower stratosphere. These factors necessitate the continued close monitoring of stratospheric ozone in upcoming decades, with a special focus on the lower stratosphere.As highly resolved data sets combining a plethora of observations with model simulations atmospheric reanalyses are, in principle, well suited for the task. All major reanalyses generate ozone output. However, significant spurious discontinuities that arise from step changes in the observing systems prevent a straightforward analysis of ozone trends and long-term variability. Building on our recent work, in this presentation we will demonstrate that trend detection is nonetheless possible using the ozone record from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis bias-corrected using a chemistry model simulation as a transfer function. Next, we will outline several strategies to reduce artificial discontinuities in the ozone record in future NASA reanalyses. This discussion will be illustrated by an example of joint assimilation of bias-corrected ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite (2004 to present) and the Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP) sensors that are expected to operate on future NOAA platforms.

Description: This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector.

Description: Like a peninsula into the Southern Ocean, the vast Patagonia desert in the southern tip of South America is exposed to extreme winds. Dust blown from this region has important impacts thousands of kilometers away, but these impacts are very difficult to assess. Questions such as the sources of dust found in snow in East Antarctica as well as the provenance of nutrients in the Southern Ocean remain unanswered. While the Patagonia desert is the likely source of dust, there is a dearth of observational records of dust activity from this desert. This study fills the gap in observations by providing a record of 50 years of surface and satellite observations of the largest and most active dust source in Patagonia: lake Colhu Huapi. The seasonality, frequency and periods of major dust activity are identified from meteorological records at a station located 100km downwind from the lake. Collocated satellite observations confirmed the major periods of dust activity in the last 30 years. This dataset provides information on how to interpret records of recent dust found in East Antarctica snow as well as help to understand the CO2 cycle in the Southern Ocean.

Description: Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

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Description: The objective of this project is to quantify changes of mangrove extent in Madagascar and Nigeria from 2015-2018. Both countries contain a significant portion of the worlds mangroves, and which are known to be deforested and degraded due to natural and anthropogenic factors. Change is estimated using multi-date Landsat-8 OLI data and cloud computational techniques. Findings show that mangroves in both countries have exhibited areal loss during the study period, but loss varies across space. Understanding the rate and magnitude of mangrove change can aid in identifying priority areas for forest regenerations, and can help construct sustainable management practices for the future.

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Description: Background (what): SI (International System of Units)-traceable Microwave Radiometer calibration; Motivation (why): NWP (Numerical Weather Prediction), FCDR (Fundamental Climate Data Record); Technology (how): NIST (National Inst. of Standards and Technology) blackbody target for ; Standards: Status & Future Plans. Development of a National Standard for Microwave Brightness Temperature (TB) at NIST.

Description: This chapter summarizes ocean color science data product requirements for the Plankton, Aerosol, Cloud,ocean Ecosystem (PACE) mission's Ocean Color Instrument (OCI) and observatory. NASA HQ delivered Level-1 science data product requirements to the PACE Project, which encompass data products to be produced and their associated uncertainties. These products and uncertainties ultimately determine the spectral nature of OCI and the performance requirements assigned to OCI and the observatory. This chapter ultimately serves to provide context for the remainder of this volume, which describes tools developed that allocate these uncertainties into their components, including allowable OCI systematic and random uncertainties, observatory geo location uncertainties, and geophysical model uncertainties.

Description: No abstract available

Description: Operating and maintaining a large multi-tenant ecosystem in the cloud requires scalable solutions to unique technical and process challenges. The Cloud Computing model grants significant permissions to development teams that traditionally were reserved for Data-Center Administrators and Supply-Chain Managers. Earthdata Cloud has worked to re-cast traditional data-center management into a sensible cloud-first model. This talk discusses some of our challenges, solutions, and way ahead.

Description: Cumulus is a scalable, extensible cloud-based archive system which is capable of ingesting, archiving, and distributing data from both existing on-prem sources and new cloud-native missions. As we have built and evolved the system with contributions from seven NASA EOSDIS organizations, we have learned several lessons about how to build a robust, broadly-applicable, microservices-based cloud system for geospatial data which we will share in this talk.

Description: Mineral dust is an integral component in the Earth system that interacts with the system's many other components involving the energy, water, and carbon cycles. Dust also degrades air quality and adversely affects human health. These interactions and impacts are not contained in regions nearby dust sources, but can reach very far because of the long-range transport on intercontinental and global scales. Satellite's routine sampling and extensive coverage in time and space makes it an ideal platform to follow the dust from sources to sinks and assess its impacts along the long journey. Dust particles are unique in their coarse size and irregular shape, which makes it feasible to distinguish them from other aerosol particles using remote sensing techniques. This talk will provide an overview of what we have learned from analyzing advanced satellite remote sensing measurements during the EOS-era supplemented by in situ observations and model simulations, including dust source characterization, seasonal and interannual variability, trans-Pacific and trans-Atlantic transport and deposition, and dust influences on the radiation budget, air quality, and ecosystems. The talk will also discuss challenges and opportunities to further improve the dust characterization and assessment of the impacts via remote sensing techniques.

Description: Temperature is a primary determining factor for plant growth and development so providing an appropriate temperature input is critically important for developing growth models. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA (US Dept. of Agriculture)-sponsored area-wide project including NASA Ames Research Center and State of California Department of Boating and Waterways, uses modeling to assess invasive aquatic plant impacts on ecosystem services in the Delta. Availability of continuous records for monitored temperature is limited and particularly in the case of water temperature the distribution of monitoring is inadequate. This work quantitatively defines the influence of air and water temperature in determining dominant growth rate processes for important floating aquatic invasive plants in the Delta. Since these plants function with portions submerged and above water we wanted to understand the relationship between root zone and shoot zone temperature and ability to use a single temperature inputs in DRAAWP models. Water Hyacinth and Primrose were gown in multiple controlled environment chamber studies with various combinations of root zone and shoot zone temperatures. Long-term growth studies provided integrated response of biomass accumulation and distribution within the canopy. Short-term gas exchange studies provide a time scale for responsiveness to temperature and a short-term study approach to evaluate temperature responses at various stages of canopy development.

Description: The NASA Goddard Earth Sciences Data and Information Services Center archives tens of thousands of Earth Observation (EO) parameters for land, atmosphere, and ocean. To facilitate GIS users to easily find, visualize, obtain, and analyze these EO data through, we developed an ArcGIS infrastructure with the Server, image services, Portal, and AOL. We will show how this capability supports broad GIS applications. Use cases including water management and air quality analyses will be demonstrated.

Description: We present a new high-resolution global composition forecast system produced by NASA's Global Modeling and Assimilation Office. The NASA Goddard Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) system combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke.

Description: We present a detailed overview of the structure and activities associated with the NASA-led ground validation component of the NASA-JAXA Global Precipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASAs GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASAs GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASAs Level-1 science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi-satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the U.S.) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed.

Description: The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Description: No abstract available

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Description: NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelveNASA Earth Observing System (EOS) data centers that process, archive, document, and distributedata from Earth science missions and related projects. The GES DISC hosts a wide range ofremotely-sensed and model data and provides reliable and robust data access and services to usersworldwide. This presentation, focusing on hydrological land surface data, provides a summary tablefor the hydrological data holdings, along with discussions of recent updates to data and data services.

Description: Volcanic lava flows and/or the gas eruptions are the most common characteristics that can be remotely monitored with satellite technology in the global perspective and on different timescales. Atmospheric Sulfur Dioxide (SO2), one of the most abundant gases from volcanic eruptions apart from atmospheric common gases Carbon Dioxide and water vapor, can be directly detected by space-based sensors on satellites. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the 12 Distributed Active Archive Centers (DAACs) within NASA's Earth Observing System Data and Information System (EOSDIS), which archives SO2 data sets from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) in 1978, till the ongoing Ozone Monitoring Instrument (OMI) on NASA's EOS-Aura satellite, the Ozone Mapping Profiler Suite (OMPS) Nadir Mapper (NM) on both the Suomi National Polar-Orbiting Partnership (Suomi-NPP or SNPP) and the Joint Polar-orbiting Satellite System-1 (JPSS-1) satellites, into the future JPSS missions. In addition to the standard OMI/Aura and OMPS/S-NPP SO2 products, SO2 products created under the charter of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, are also archived at GES DISC, through which NASA enacts to expand understanding the Earth system using consistent data records. The Land Processes Distributed Active Archive Center (LP DAAC) is another EOSDIS's DAAC that provides land data products and operates as a partnership with the U.S. Geological Survey (USGS). The LP DAAC has been archiving the satellite imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard NASA's EOS-Terra satellite, a high spatial resolution (15 meters) and 14 band multispectral instrument. The ASTER imagery is one of the land products contributing to the application for monitoring hot spots and land terrain changes caused by volcanic eruption events. The data potential in GES DISC and LP DAAC to monitor volcanic sources of SO2 and the influence of wind fields on the gas plume spread will be demonstrated with the most recent 2018 May-July Kilauea Volcano eruption.

Description: Crafting a great user experience is hard. Crafting a great user experience for Earth science applications is fraught with challenges. From the variability in metadata to the experience profile of various users the possible permutations of use cases introduce layer upon layer of complexities that must be designed against. In this session, the Earthdata Search team would like to highlight lessons learned over the lifespan of the application the good, the bad, and the ugly.

Description: This presentation provides mission operations status for the Earth Observing System (EOS) Aqua satellite for the past six-months (December 2018 through May 2019). It only contains information that is of interest to the International Earth Science Constellation (ESC) Mission Operations Working Group (MOWG) member missions. It will be presented at the bi-annual MOWG Meeting in Toulouse, France on Wednesday, June 5, 2019. These meetings have been occurring twice a year since the MOWG was formed in 2003.

Description: No abstract available

Description: Interannual climate variability patterns associated with the El Nio-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 20152016 El Nio event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Nio-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (1481% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.528% higher during years with El Nio events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p 〈 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p 〈 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

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Description: High accuracy achieved with temperature stabilized unfiltered trap detectors illuminated by monochromatic light. Tunable lasers and optical parametric oscillators provide orders of magnitude higher spectral radiance than blackbody or other broadband sources; calibrate at high signal levels.

Description: The Geostationary Operational Environmental Satellite (GOES)-16 observatory was launched on 19 November 2016. During daily on-orbit operations, shadowing of the inboard magnetometer sensor unit occurs due to spacecraft geometry and solar angle. Throughout the shadowing periods of the inboard magnetometer, anomalous excursions of 20 nanotesla (nT) are observed. In addition to the excursions during shadow events, the measurement difference between the inboard and outboard magnetometer varies over the day, indicating erroneous measurements by one or both magnetometers. In addition, based on the deployment rotations, the zero offsets of the X and Y axes were found to be significantly different, ~30nT, from ground calibration data. Because of these observations, an extensive root cause investigation was undertaken to correct the magnetometer system for the next spacecraft in the GOES-R series. This paper documents the efforts of that activity and the lessons learned as a result of the investigation.

Description: The most recent Decadal Survey placed high value on continuing constellation science. The ESC has evolved by seeing new missions joining and old missions retiring. Most recently, GCOM-W1, Landsat-8, and OCO-2 joined during 2012-2014. Landsat-9 is set to join in 2020. Each new mission provides new and improved suite of sensors. The new sensors also benefit both from the multitude of other existing on-orbit sensors as well as from the long-term cross-calibrated climate observations from the sensors that preceded them. At the same time, existing missions leave the constellation due to low fuel reserves or aging spacecraft subsystems. For example, CloudSat and CALIPSO left the ESC orbits in 2018, although they plan to continue making coordinated science observations at their new lower altitudes. This ESC evolution is expected to continue and this paper will discuss the opportunities for other new missions to join the ESC.

Description: No abstract available

Description: Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning, and implementation of management for aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project including NASA Ames Research Center and State of California Department of Boating and Waterways, is working to enhance decision-making and operational efficiency of invasive plant management in the California Sacramento-San Joaquin Delta. Expansion of invasive aquatic plants has been detrimental to water management and the ecosystem complex in the San Francisco Bay/California Delta. The portion of DRAAWP reported here focuses on parametrizing the environmental response inputs for the Delta models for prominent invasive aquatic plants. Changing climate, long-term drought, shifts in land use, and variation in water flow and quality from input watersheds lead to wide and unique variation in environmental conditions. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response of invasive aquatic plants are examined using controlled environment growth facilities at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature, nutrient, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology.

Description: There is considerable interest in the aggregate methane emissions from the Amazon and similar moist tropical regions, and XCH4 measurements are well suited to constrain sources to the global atmosphere. Similarly, XCO2 measurements constrain CO2 in the region. XCO helps to partition CO2 patterns among burning and respiration processes. GeoCarb may allow these column measurements over the Western Hemisphere, but satellite retrieval require exacting calibration and validation by sun-focused Fourier transform spectrometers (FTS). The rarity of sufficiently large gaps in the cloud cover over the Amazon and similar rainforests restricts the validation opportunities for useful FTS observations and even more the opportunities for accurate retrievals. TropOMI observational statistics are extremely poor for the region. We have used two data sources to evaluate FTS opportunities at Manaus, Brazil, an FTS operated for 8 months near Manaus by Mavendra Dubey, and also sun-photometer measurements at several stations. The promise of using data from other satellites, e.g. GOES-16 ( (Geostationary Operational Environmental Satellite) and CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization gaps and aerosol layering will be described. We report initial results on five questions: (1) how frequent are observing opportunities of FTS?, (2) What evidence is there that gaps in clouds are wide enough for satellite retrievals at an appropriate accuracy, (3) What is the diurnal and seasonal variability of cloud gaps?, and (4) What limitations are currently suggested for unbiased FTS measurement of XCH4 due to diurnal effects, and (5) What evidence is there for incidence of problematic high aerosol extinction at higher layers of the troposphere (800 hPa to 120 hPa) which alter the XCH4 light-paths?

Description: Extreme weather and climate events, such as heavy rainfall, heatwave, floods and droughts, and strong wind, can have devastating impacts on society. NASA and NOAA, based on independent analyses, recently announced that global surface temperatures in 2018 are the fourth warmest since 1880, behind only those of 2016, 2017, and 2015 (nasa.gov). Also in 2018, the United States experienced 14 billion-dollar disasters, ranking as the fourth highest total number of such events, behind only the years 2017, 2011, and 2016 (climate.gov). Many research studies have focused on acquiring observational and modeling data, to reveal linkages between increasing extreme events, global water and energy cycle, and global climate change. However, draw conclusions is still a challenge. NASA Goddard Earth Sciences Data and Information Services Center is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remotely-sensed and model data and provides reliable and robust data access and services to users worldwide. This presentation provides a few examples of extreme event study that use Land Surface Model (LSM) assimilated, quality-controlled, and spatially and temporally consistent, hydrological data from the GES DISC. Also provided is a summary table for the hydrological data holdings, along with discussions of recent updates to data and data services.

Description: NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.

Description: Urban ecosystems interact with surroundings via land cover changes and their subsequent impact on surface temperature. In emerging countries, large urban agglomerations often form around cities, and only few studies have evaluated their impact. This study carries out the first ever large-scale assessment of urban heat island (UHI) and reflects on its mitigation in Morocco.The analysis reveals a well-defined UHI in urban-areas built within vegetated lands and an urban heat sink (UHS) in urban-areas built within arid regions. Both UHI and UHS amplitudes are higher during day than nighttime, emphasizing vegetation physiological activity. We show a monotonic increase in UHI amplitude with urban-area size. However, unlike previous studies, our analysis shows that as urban-areas built in desert-like environments grow in size, the UHS gradually decreases to ultimately turn into an UHI. On average, cities built within vegetation are warmer than rural fringe by 1.51C during daytime. This suggests that daytime urban heating may exacerbate the potential climate warming. Our results also suggest that adapted trees constitute a natural cooling mechanism and should be part of urban heating mitigation in Morocco.

Description: NASAs Earth Science Division (ESD) seeks to develop a scientific understanding of Earth and its response to natural and human-induced changes. Earth is a system comprised of diverse components interacting in complex ways. Understanding Earths atmosphere, surface and interior, oceans and surface water, ice and snow, and life as a single connected system is necessary in order to improve our predictions of climate, weather, and natural hazards. The ESDs Flight Program consists of a coordinated series of satellite and airborne systems providing long and short-term, global and regional observations. In addition, the Flight Program provides infrastructure for operating these missions, processing their scientific data, and distributing them on a free and open basis to researchers, operational users, and the public. The Flight Program currently has 24 operating Earth observing space missions and instruments. There are 18 more missions and instruments planned for launch over the next five years. These comprise missions recommended by the National Academies 2017 Earth Science Decadal Survey, missions and selected instruments to ensure availability of key climate data sets, operational missions to sustain the land imaging provided by the Landsat system, and small-sized competitively selected orbital and instrument missions of opportunity belonging to the Earth Venture (EV) program. The Earth Science Decadal Survey, released in early 2018, recommended four new Flight Program elements in addition to the above activities that comprise the Program of Record (POR). Small satellites (~500 kg or less) are essential components of these activities. Presently, there is an increasing use of micro and nanosatellites (or CubeSats) in constellations to support NASA ESDs scientific objectives. These include the Cyclone Global Navigation Satellite System (CYGNSS) for observing tropical cyclone intensification and genesis factors, the Timed-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, and the Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) CubeSat mission. ESD small satellite initiatives like the Small Satellite Constellation Data Buy and Venture Class Launch Services (VCLS) are also underway. The Earth Science Technology Offices (ESTO) In-Space Validation of Earth Science Technologies (InVEST) and the Venture Technology program elements have launched seven 3U and 6U CubeSat missions to validate advanced instruments and related technologies. An equivalent number of InVEST and other technology demonstration CubeSats are being prepared for launch in the next year. An overview of plans and current status including topics related to small satellite enabling activities will be presented.

Description: No abstract available

Description: Developments in ocean data assimilation (DA) and observing system technologies are intertwined. New observation types lead to new DA methods, and new DA methods such as Coupled Data Assimilation can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners are encouraged to make better use of observations that are already available, for example in strongly coupled data assimilation where ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate,as well as initializing operational long-range prediction models. There are remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean observing system throughout its history, the presence of biases and drifts in models, and simplifying assumptions made in the DA methods. From a governance point of view, more support is needed to interface the observing community and the ocean DA community. For prediction applications, the ocean DA community must work with the ocean observing community to establish protocols for rapid communication of ocean observing data on NWP timescales. There is potential for new observations to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of numerical weather prediction covering hours to weeks, out to multiple decades. It is highly encouraged that communication be fostered between thesecommunities to allow operational prediction centers the ability to provide guidance to the design of a sustained and adaptive observing network.

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Description: Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Yukon River basin in interior Alaska. Statistical analysis of changes in the NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires, commonly affecting shrubland and forested ecosystems at relatively low elevations (〈 300 m).

Description: The Arctic National Wildlife Refuge (ANWR) was established in 1980 and covers 19 million acres (77,000 km2) in northeast Alaska. Wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the arctic provide limited time for species to recover. Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in all ecoregions of the ANWR. Statistical analysis of changes in each MODIS pixel NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST) to map regional change rates. Results suggested that most negative NDVI anomalies in the tundra-covered river drainages of the Brooks Range Mountains and coastal plain have been associated with early spring thawing and elevated levels of surface moisture in low elevation drainages of the northern ANWR ecoregions.

Description: In this paper, we explore a time series approach to using the tau-omega (-) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor and an offset factor . By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, and can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of and can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) ofSMAP brightness temperature observations.

Description: Cross-track microwave sounders make up a significant percentage of the radiometers included in the Global Precipitation Measurement (GPM) constellation. Therefore, it is important to properly assess the calibration of each sounder instrument and to understand the impact of the calibration on the derived precipitation rates. This ensures an accurate precipitation product is produced for the entire constellation. This paper will use data from past and current microwave sounders to show how offsets in the calibration can impact the precipitation using the GPM Level 2 GPROF algorithm. Potential improvements to the instrument calibration will be assessed by analyzing how they would positively impact the precipitation trends and agreement among the constellation sensors.

Description: Global, 3-hourly, 9-km resolution soil moisture estimates are available with a mean latency of ~2.5 days from the NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product. These estimates are based on the assimilation of SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Routine monitoring of the L4_SM system's assimilation diagnostics revealed occasionally large observation-minus-forecast Tb differences across eastern central Australia that resulted in large analysis increments (or adjustments) of the model forecast soil moisture. Because this region lacks in situ soil moisture measurements, we developed an alternative approach to assess the veracity of the soil moisture analysis increments in the L4_SM system. Using regional gauge-based precipitation data, we demonstrate that the L4_SM soil moisture increments are correlated with errors in the L4_SM precipitation forcing, suggesting that the SMAP Tb observations contribute valuable information to the L4_SM soil moisture estimates.

Description: Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regionalagricultural drought monitoring is one of the most logicalapplication areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimetersof the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entireroot zone. The assimilation of remotely sensed soil moisture productsinto continuous soil water balance models provides a way ofaddressing this shortcoming. Here, we describe the assimilationof NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impactof SMAP on USDA FAS drought monitoring capabilities. Theassimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for randomerrors inherent in its rainfall forcing data. The performanceof this SMAP-based assimilation system is evaluated using two approaches.At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moistureand the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimatesis carried out at seven of the SMAP core Cal/Val sites located in theUSA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Description: Experimental evolution (EE) exposes microbes to intentional stressors to improve resistance through artificial mutation. The resulting changes to metabolic pathways, protein structure, and genetic sequences, along with traditional genetic engineering tools, to can help understand the mechanisms of improved tolerance. An automated experimental set-up -- the Automated Adaptive Directed Evolution Chamber (AADEC) -- with minimal scope for human interference was developed at NASA Ames. A second- generation device integrating more real-time biochemical sensors has been developed recently. Added sensors include pH for indicating metabolic products, oxidation-reduction potential (ORP) for indicating available/consumed metabolic energy, dissolved oxygen (DO) for indicating aerobic/anaerobic growth cycles, and electrical conductivity (EC) as an additional indicator of metabolic products. With four additional sensors, the system is biochemically more informative in real-time. More importantly, each sensor parameter can be used as a selection pressure, individually or in combination with others, to artificially create and control inhospitable environments analogous to extremophile habitats for microbial growth in the lab. Potential stressors to be added in the future include thermal, reactive oxygen species, metal-ion concentrations, and varying nutrient availability.

Description: The NASA Global Modeling and Assimilation Office (GMAO) has conducted a series of 40-day nonhydrostatic global simulations with horizontal grid spacing ranging from 200 km to 3 km, as part of the DYAMOND model intercomparison project. The Goddard Earth Observing System (GEOS) model was run with the Grell-Freitas scale-aware convection scheme, which smoothly reduces parameterized deep convection with increasing resolution. Here we evaluate the diurnal cycle and other statistics of precipitation and organized convection as a function of resolution. For validation we use the 0.1 degree IMERG precipitation and 4 km Merged IR brightness temperature datasets, focusing on four regions: the continental United States, Amazonia, the equatorial Indian ocean, and the Maritime Continent. Early results indicate good phase agreement but excessive magnitude of the continental diurnal cycle of precipitation at coarser resolutions, with improved magnitude as resolution increases and the role of parameterization is reduced. Convective cloud clusters are identified with a brightness temperature threshold, and we find realistic numbers of the largest clusters (〉10^4 km sq) at all resolutions, while the number of smaller clusters increases with resolution, approaching observations when dx=3 km. The observed diurnal cycle in the cluster size distribution is also reproduced, with realistic magnitude in the highest resolution runs. Precipitation characteristics across cluster sizes are also examined. The results show the potential for global mesoscale simulations as a community science resource, and we invite collaboration to explore these runs in greater detail.

Description: As sea levels rise, low-lying coastal forests increasingly are subject to stressors such as inundation and saltwater exposure. At long timescales (for example, centuries), the extent of inundation and saltwater exposure will increase; however, on a decadal timescale, the role of these drivers may differ in both magnitude and direction. To investigate the drivers of decadal-scale vegetation change, we measured the changes in five metrics of vegetation composition and structure between 2003/2004 and 2016/ 2017 at 98 plots distributed across a vegetation gradient from coastal forest to brackish marshes (〈 0.518 ppt). We used elevation as a proxy of inundation vulnerability and soil sodium concentration as a proxy of saltwater exposure, and we investigated relationships between these two variables and the change in vegetation conditions between the two sampling periods. Soil sodium concentration was a significant predictor of vegetation change for all five vegetation metrics, whereas the effect of elevation was not significant for any of the metrics. The one site that was affected by wildfire twice during the duration of the study shifted almost completely from forest to marsh with limited regeneration ofwoody vegetation observed in 2016/ 2017. Our results show that salinization in our system is a more important driver of vegetation change than inundation potential. Furthermore, the effects of drought-induced salinization could be amplified by the elevated risk of wildfire during droughts. Forecasting the response of coastal wetlands to rising sea levels will require a better understanding of the individual and combined effects of salinity, droughts, and wildfires on vegetation.

Description: There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (20092012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.740.77 ppm for every 10 of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a southnorth seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (9 d per 10 latitude for fall segments and 10 d per 10 latitude for rise segments) and suggest that a specific latitude (2 N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Description: The present invention relates to uncooled microbolometers which can be integrated in future thermal instruments engaged in land imaging on future observatories. The present invention includes: (1) developing and characterizing a microstructured VOx thin film, and, (2) fabricating an uncooled microbolometer array over the 8-14 micron spectral band.

Description: Analysis was performed to determine whether a lightning flash could be associated with every reported lightning-initiated wildfire that grew to at least 4 km(exp 2). In total, 905 lightning-initiated wildfires within the Continental United States (CONUS) between 2012 and 2015 were analyzed. Fixed and fire radius search methods showed that 8188% of wildfires had a corresponding lightning flash within a 14 day period prior to the report date. The two methods showed that 5260% of lightning-initiated wildfires were reported on the same day as the closest lightning flash. The fire radius method indicated the most promising spatial results, where the median distance between the closest lightning and the wildfire start location was 0.83 km, followed by a 75th percentile of 1.6 km and a 95th percentile of 5.86 km. Ninety percent of the closest lightning flashes to wildfires were negative polarity. Maximum flash densities were less than 0.41 flashes km(exp 2) for the 24 h period at the fire start location. The majority of lightning-initiated holdover events were observed in the Western CONUS, with a peak density in north-central Idaho. A twelve day holdover event in New Mexico was also discussed, outlining the opportunities and limitations of using lightning data to characterize wildfires.

Description: A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (35 days) at moderate spatial resolution (1030 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASAs Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 20162018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.

Description: In summer 2018, FELDSPAR conducted an analog sampling expedition to the Holuhraun volcanoin the central highlands of Iceland that erupted from August 2014 to February 2015. Holuhraun hassteep gradients within an otherwise relatively controlled region, and thus served as the primaryMars analog site for the 2018 FELDSPAR field campaign. Samples along gradients of mineralogyas measured via near-IR and visible reflectance spectroscopy, temperature as measured by an IRprobe, and physical location about the fissure (slope incline, directionality, height up the slope,etc.) were analyzed in-field with reflectance spectroscopy and X-ray fluorescence, in the field labfor adenosine trisphosphate (ATP) as an indicator of metabolic activity, and after return to thehome lab for both geochemical and geophysical parameters (moisture content, grain size, X-raydiffraction) and biochemical parameters (DNA content and speciation). This abstract representsthe first report on the 2018 field campaign with initial results and interpretation. This work is part ofField Exploration and Life Detection Sampling for Planetary Analogue Research (FELDSPAR), onFacebook @FELDSPAResearch.

Description: Atmospheric research in the Earth Sciences Division (610) consists of research and technology development programs dedicated to advancing knowledge and understanding of the atmosphere and its interaction with the climate of Earth. The Divisions goals are to improve understanding of the dynamics and physical properties of precipitation, clouds, and aerosols; atmospheric chemistry, including the role of natural and anthropogenic trace species on the ozone balance in the stratosphere and the troposphere; and radiative properties of Earths atmosphere and the influence of solar variability on the Earths climate. Major research activities are carried out in the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office. The overall scope of the research covers an end-to-end process, starting with the identification of scientific problems, leading to observation requirements for remote sensing platforms, technology and retrieval algorithm development; followed by flight projects and satellite missions; and eventually, resulting in data processing, analyses of measurements, and dissemination from flight projects and missions.

Description: A 15 minute presentation overview of GES DISC status presented to the Aura Data Systems Working Group since the last meeting in 2016. Status of OMI products in archive, MLS products in archive, GES DISC service and activites status, introduction to new Data Publication System (DPS), and a reminder on Data Preservation.

Description: Many NASA Earth Observing System (EOS) have either already reached the end of their active life or are nearing it. Preservmissionsation of data products is a fairly well defined task for the NASA EOS Data Centers or DAACs.The Goddard Earth Sciences Data and Information Services Center (GES-DISC) has implemented a repository system, which is capable of long-term archive of documentation artifacts and other associated digital content. The existing GES-DISC Repository System is based on Fedora Commons, an open-source repository management software, for cost savings and flexibility.The first mission to utilize the GES-DISC Repository System was the High Resolution Dynamics Limb Sounder (HIRDLS) on the Aura spacecraft. Since then, the GES DISC has gathered documentation from the UARS and TOMS into the Repository. The Microwave Limb Sounder (MLS) team has begun delivering some early pre-launch documents to the GES-DISC Repository System as well. Other missions in planning or progress include AIRS, OMI, SORCE, SNPP Sounder, and TRMM.

Description: The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA's LTDR (Land Long Term Data Record) between 1981-2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

Description: Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

Description: No abstract available

Description: The MODIS instruments on the Terra and Aqua spacecraft use a sunlit solar diffuser (SD), with an optional SD attenuation screen (SDS), to calibrate the reflective solar bands. A solar diffuser stability monitor (SDSM) is used to track the SD reflectance degradation on orbit, by taking a ratio of the detector response when viewing the SD compared to the response when viewing the sun. The MODIS SDSMs have been operated both with and without the SDS in place. The SDSMs have also been operated in both a fixed and an alternating mode. In the alternating mode, the SDSM detectors view the SD, sun, and a dark background in an alternating pattern with the view changing on every MODIS scan within a single orbit. In the fixed mode, the SDSM detectors are fixed on the sun view for one orbit, and then are fixed on the SD view for the following orbit. This paper reviews the history of the SDSM operational configurations used throughout the MODIS missions and discusses the differences in the SD degradation results, which may be due to differences in sun-satellite geometry, SD signal level, and stray light effects. We highlight Aqua SDSM results from two recent dates in October 2017 and July 2019, where both the fixed and alternating mode calibrations were run on the same day, providing clear examples of the calibration differences. Additionally, we show how mixing the results from calibrations done with and without the SDS for Aqua MODIS can provide more stable results.

Description: Radiative transfer modeling is an important tool for interpreting remote sensing observations. It allows us to determine how sensor characteristics will impact observations, and it gives us a framework for us to test assumptions about the phenomena we are attempting to observe. In this work, we use cloud simulations for precipitation events observed during various GPM-related field campaigns. The simulations show how various properties of clouds and precipitation affect the measurements.

Description: Cross-track microwave sounders make up a significant percentage of the radiometers included in the Global Precipitation Measurement (GPM) constellation. Therefore, it is important to properly assess the calibration of each sounder instrument and to understand the impact of the calibration on the derived precipitation rates. This ensures an accurate precipitation product is produced for the entire constellation. This paper will use data from past and current microwave sounders to show how offsets in the calibration can impact the precipitation using the GPM Level 2 GPROF algorithm. Potential improvements to the instrument calibration will be assessed by analyzing how they would positively impact the precipitation trends and agreement among the constellation sensors.

Description: A rapidly developing "flash drought" occurred over the US Northern Plains in the summer of 2017, spurred by unusually high temperatures and strong evaporative demand. The impacts of the drought included widespread reductions in rangeland and agricultural productivity that cascaded into significant economic losses. Here, we used satellite information from the NASA Soil Moisture Active Passive (SMAP) mission to clarify the nature and impact of the drought on regional vegetation growth. The model enhanced SMAP Level 4 Soil Moisture (L4SM) and Carbon (L4C) products were used with other ancillary data to examine spatial and seasonal anomalies in surface to root zone soil moisture and vegetation productivity (GPP). We find that the flash drought was triggered by a mid-July heat wave, conditioned by exceptionally low spring rainfall. The drought resulted in anomalous low soil moisture levels and regional GPP collapse, coinciding with severe (D3) to exceptional (D4) drought conditions indicated from the US Drought Monitor. The SMAP L4C GPP anomalies closely tracked reported county-level crop production anomalies for the major regional crop types, indicating generally larger productivity decline in managed croplands than surrounding natural areas. The SMAP L4 global products provide an effective indicator of vegetation growth changes and moisture-related restrictions on ecosystem productivity that are complementary with more traditional drought assessment tools.

Description: Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At Wband, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors.

Description: Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbonfluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle,we must also be able to understand and model its effects on dryland vegetation. Dynamic Global VegetationModels (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduceseasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to bothtotal productivity and competitive interactions with other plants, errors in how process-based models representphenology hinder our ability to predict climate change impacts. This may be particularly problematic in drylandecosystems where many species have developed a complex phenology in response to seasonal variability in bothmoisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure ofexisting phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) andgross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized modelparameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, wemodified the model to include a semi-deciduous phenology type and added flexibility to the representation ofgrass phenology. While optimizing parameters reduced model bias, the largest gains in model performance wereassociated with the development of our new representation of phenology. This modified model was able to bettercapture seasonal patterns of both leaf area index (R2=0.75) and gross primary productivity (R2=0.84), thoughits ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new modelalso resulted in a more realistic outcome of modeled competition between grass and shrubs. These findingsdemonstrate the importance of improving how DGVMs represent phenology in order to accurately forecastclimate change impacts in dryland ecosystems.

Description: Burned landscapes present difficult hydrologic forecasting challenges for National Weather Service Offices. Burned soils and landscapes can be conducive to the development of flash flooding and landslides from heavy precipitation events. The severity of the burn scar can be directly related to the risk for debris flows and flash flooding. Burned Area Reflectance Classification (BARC) map is generated to indicate the degree of burn severity, which is generated initially by high-resolution satellite imagery from sources such as Landsat, and later by labor-intensive efforts conducted at the burn scar by Burned Area Emergency Response (BAER) teams. The challenge for operational meteorologists is that these sources of information are not readily available in near real-time. Landsat imagery, for example, may only be available about once every eight days, and cloudy conditions can obstruct the observation of the burn scar during a single pass. BAER teams cannot conduct assessments until the wildfire has been at least 40 percent contained (up to 80 percent in some regions), and the process itself can take further days to weeks to complete depending on a number of factors. To help remedy this lapse in knowledge, NASA SPoRT has developed the generation of NBR imagery in the Advanced Weather Interactive Processing System (AWIPS) using data from the operational GOES 16 and 17 satellites and S-NPP. This presentation will discuss the development of the GOES- and SNPP-derived NBR and dNBR imagery and their initial evaluation by real-time decision makers.

Description: The Aquarius/SAC-D mission operated between August 2011 and June 2015 with the main goal of providing global estimates of sea surface salinity (SSS). It comprised both active and passive microwave sensors operating at L-band to observe the same surface area almost simultaneously. Measurements from both instruments underwent subsequent filtering to mitigate the effect of Radio Frequency Interference (RFI). This report describes the analysis of statistics of RFI in samples acquired by the Aquarius radiometers, and its results could be used to improve the performance of the interference detection algorithm.

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Description: Information about inundated areas is critical for distributing aid and resources in flood emergency response operations. Conventional methods of monitoring floods, like gauge based observations and reports from local authorities, provide very detailed and accurate information about flood depth and location. However, the geographic coverage of these point-based observations is limited and delays are common. Satellite-based images can help address these challenges, providing near real-time flood extent information over large areas of coverage. The Hydrological Remote Sensing Analysis of Floods (HYDRAFloods) tool, currently being developed by SERVIR-Mekong in collaboration with the Myanmar Department of Disaster Management, is one such example. Generating flood maps, even from satellite imagery, is challenging, given the many disparate sources of information. HYDRAFloods leverages the most recently available remotely sensed data acquired by multiple satellite platforms to automate the creation of daily flood maps. Through combining multiple satellite sources, including optical, microwave, and synthetic aperture radar datasets, near real-time flood maps with reduced cloud impact and increased satellite observations can be generated for use by disaster managers.

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Description: The Thermal Infrared Sensor 2 (TIRS-2) pre-launch spectral characterization at telescope and detector subsystem level provided a strong indication that the spectral response requirements will be met. Confidence in the validity of these results was strengthened through comparison to component-level measurements. This work reviews the modifications to the test setup to reduce spectral response uncertainties further in preparation for instrument-level testing. We developed a methodology to improve alignment repeatability of the upgraded system. Preliminary indications show that the spectral response will contribute a relatively small amount to the overall TIRS-2 radiometric uncertainty budget, which is expected to meet its radiometric requirements. Through spectral response testing, as part of a comprehensive pre-launch test program, TIRS-2 is expected to achieve the performance necessary for a variety of environmental applications.