ALBERT

Description: The NASA Global Imagery Browse Services (GIBS) and Worldview interactive mapping site leverage scientific and community best practices, open source software, and public standards to provide a scalable, compliant, and authoritative source for NASA Earth Observing System (EOS) Earth science data visualizations. GIBS and Worldview allow end users to easily and quickly interact with more than 800 full resolution pre-generated raster- and vector-based visualizations. This interactive discovery approach relies on visual observation and identification of phenomena that are not as simply identified otherwise. This eLightning presentation will exhibit the broad set of capabilities and visualization layers made possible through the GIBS and Worldview open source software. Specific dependencies on, and contributions to, open source software will be highlighted. Additionally, opportunities for future improvements for better interoperability and reuse through open source software will be discussed.

Description: The Alpha Jet Atmospheric eXperiment (AJAX) airborne science project based out of NASA Ames Research Center performed eight science flights in coordination with the California Baseline Ozone Transport Study (CABOTS) campaign. Many of these flights included a series of vertical profiles (~ 0-5 km) distributed roughly along either a North/South or East/West transect. Some flights also connected the fixed-location measurements at Visalia (TOPAZ ozone lidar) and Bodega Bay (ozonesondes). AJAX measured ozone, carbon dioxide, methane, water vapor, and 3-D winds on each flight, and those in situ measurements are the basis of the data sets collected here. Trace gas data sets including time and aircraft position have been delivered as comma-separated-value text files. Meteorological data (temperature, pressure and 3-dimensional winds) are provided at 1 Hz in ICARTT-compliant text files.

Description: Northern peatlands are an integral part of the global carbon cyclea strong sink of atmospheric carbon dioxide and source of methane. Increasing anthropogenic carbon dioxide and methane in the atmosphere are thought to strongly impact these environments, and yet, peatlands are not routinely included in Earth system models. Here we present a quantification of the sink and stock of northern peat carbon from the last glacial period through the pre-industrial period. Additional data and new algorithms for reconstructing the history of peat carbon accumulation and the timing of peatland initiation increased the estimate of total northern peat carbon stocks from 545 Gt to 1,055 Gt of carbon. Further, the post-glacial increases in peatland initiation rate and carbon accumulation rate are more abrupt than previously reported. Peatlands have been a strong carbon sink throughout the Holocene, but the atmospheric partial pressure of carbon dioxide has been relatively stable over this period. While processes such as permafrost thaw and coral reef development probably contributed some additional carbon to the atmosphere, we suggest that deep ocean upwelling was the most important mechanism for balancing the peatland sink and maintaining the observed stability.

Description: Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

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Description: Spaceborne lidar observations have great potential to provide accurate global estimates of the aerosol direct radiative effect (DRE) in both clear and cloudy conditions. However, comparisons between observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) and multiple years of Atmospheric Radiation Measurement (ARM) programs ground-based Raman lidars (RL) show that CALIPSO does not detect all radiatively significant aerosol, i.e. aerosol that directly modifies the Earths radiation budget. We estimated that using CALIPSO observations results in an underestimate of the magnitude of the global mean aerosol DRE by up to 54%. The ARM RL datasets along with NASA Langley airborne high spectral resolution lidar (HSRL) data from multiple field campaigns are used to compute the detection sensitivity required to accurately resolve the aerosol DRE. This shows that a lidar with a backscatter coefficient detection sensitivity of about 12x10(exp -4)km(exp -1)sr(exp -1) at 532nm would resolve all the aerosol needed to derive the DRE to within 1%.

Description: Adjoint models are powerful tools that can be used to estimate the impact of observations on a chosen norm for numerical weather prediction forecasts. In this study, the Global Modeling and Assimilation Office (NASA/GMAO) Observing System Simulation Experiment framework is employed to investigate the behavior of the adjoint tool in an environment where the 'true' state of the atmosphere is fully known. This allows for the calculation of adjoint estimates of observation impact for very short forecast times including the zero-hour analysis state. The adjoint calculations using self-analysis verification can also be compared to adjoint calculations using the 'truth' as verification in order to characterize the robustness of adjoint estimations in the operational setting. Results from a experiments exploring various aspects of performance of the adjoint tool will be presented.

Description: Some of the most intense thunderstorms on the planet occur in the Hindu Kush Himalaya (HKH) region of South-Central Asia. NASA/SERVIR Applied Sciences Team competitive project to develop capacity of severe thunderstorm monitoring and forecasting tool for HKH. Project Goal: Use [NASA] modeling and remote-sensing assets to build early warning capabilities and facilitate timely disaster response for high impact weather events in the HKH region. Specific objectives: 1. Prototype and transition High-Impact Weather Assessment Toolkit (HIWAT) 2. Jointly develop HIWAT capabilities & training with SERVIRs hub in Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD) 3. Demonstrate capacity in end-user environment 4. Transition HIWAT system to ICIMOD for future maintenance.

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Description: We are creating a new algorithm that combines observations from MISR and MODIS (both on the NASA Terra spacecraft) to improve atmospheric correction and coverage for ocean color data products. The algorithm utilizes information rich, multi-angle MISR observations for atmospheric correction, applied to MODIS. Our goal is to produce atmospherically corrected Remote Sensing Reflectance from MODIS with enhanced coverage and accuracy, for input to downstream bio-optical ocean parameter retrieval algorithms.An important aspect of this work is the utilization of multi-angle views of the reflected ocean surface sun glint. Usually, such observations are avoided, since the intensity of the glint overwhelms any contribution from the ocean body. However, MISR's multi-angle observations see varying degrees of glint, which means they can be used to better determine aerosol optical properties (Kaufman et al., 2002, Ottaviani et al., 2013), and to identify surface wind speeds that govern the glint pattern. The latter could be utilized to replace the wind speeds taken from ancillary sources that are currently used to conservatively mask potential glint contamination in MODIS observations.To assess this capability, and to identify the appropriate parameterization, we present an analysis using the Generalized Nonlinear Retrieval Analysis (GENRA, Vukicevic et al., 2009) information content assessment. This technique is also easily modified to act as a Bayesian retrieval algorithm, for which initial results are discussed. Finally, we describe the status of integrating MISR data into the processing capabilities of the Ocean Biology Processing Group (OBPG) at NASA, and show the first ocean color vicarious calibration (Franz et al., 2007) of the MISR instrument.

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Description: NASA has been monitoring ozone variations from space since 1970 with the launch of the Backscatter Ultraviolet instrument (BUV) on Nimbus- 4. Ozone in the Earths stratosphere and troposphere plays a crucial role in protecting life from harmful solar UV radiation, and it also influences Earths climate. The vertical distribution of ozone is used in modeling forecasts, verifying model analysis and simulations, and improving the measurement of tropospheric ozone that is a toxic constituent of air pollution. The Microwave Limb Sounder (MLS) on the EOS Aura spacecraft has been monitoring long-term continuous stratosphere ozone vertical behaviors since 2004. The Ozone Mapper Profile Suite (OMPS) on the Suomi National Polar-orbiting Partnership (S-NPP) was launched in 2011. Two of its three instruments, the Limb Profiler (LP) and the Nadir Profiler (NP), are designed to map ozone profiles with full global coverage (~6 days for NP) and a high level of vertical accuracy (1-km reporting record for LP) in the stratosphere and upper troposphere. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5 Precursor (S5P), launched in 2017, aims to retrieve ozone profiles in both the troposphere and up to top-of-atmosphere (TOA) by using UV radiation measurements in a spectral range of 270-320 nm. S5P/TROPOMI ozone profile products are planned to become available in April 2020. This presentation will concentrate on satellite ozone vertical profile data collections from OMPS and MLS which are archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). TROPOMI ozone vertical profiles for the troposphere and to top-of-atmosphere will become available at NASA GES DISC in 2020. The presentation briefly explores their potential complementary usability in determining three-dimensional ozone global distributions with high temporal and spatial resolutions.

Description: We investigate satellite orbital drag effects at lowEarth orbit associated with thermosphere heating during magnetic storms caused by coronal mass ejections. CHAllenge Minisatellite Payload (CHAMP) and Gravity Recovery And Climate Experiment (GRACE) neutral density data are used to compute orbital drag. Stormtoquiet density comparisons are performed with background densities obtained by the JacchiaBowman 2008 (JB2008) empirical model. Our storms are grouped in different categories regarding their intensities as indicated by minimum values of the SYMH index. We then perform superposed epoch analyses with storm main phase onset as zero epoch time. In general, we find that orbital drag effects are larger for CHAMP (lower altitudes) in comparison to GRACE (higher altitudes). Results show that storm time drag effects manifest first at high latitudes, but for extreme storms, particularly observed by GRACE, stronger orbital drag effects occur during early main phase at low/equatorial latitudes, probably due to heating propagation from high latitudes. We find that storm time orbital decay along the satellites' path generally increases with storm intensity, being stronger and faster for the most extreme events. For these events, orbital drag effects decrease faster probably due to elevated cooling effects caused by nitric oxide, which introduce modeled density uncertainties during storm recovery phase. Errors associated with total orbit decay introduced by JB2008 are generally the largest for the strongest storms and increase during storm times, particular during recovery phases. We discuss the implication of these uncertainties for the prediction of collision between space objects at lowEarth orbit during magnetic storms.

Description: The Image navigation and registration (INR) Performance Assessment Tool Set (IPATS) measurement accuracy, within 0.06 pixels, is sufficient for INR assessment. IPATS is not a static system. Additional filters and/or sub-procedures were developed when the demand emerged, e.g. the development of Short Term AbNormal Dectection (STAND) and View Zenith Angle (VZA) filters in post-launch test (PLT) of GOES-16 and GOES-17 respectively. NAV INR accuracy improved with updates and tuning in PLT. Currently, NAV errors are about 1-2 urad for all assessed channels of both ABIs. IPATS NAV assessments will continue to provide feedback for tuning the navigation algorithms and parameters in future updates and future GOES-R series ABIs.

Description: The first two satellites of the US Geostationary Operational Environmental Satellite R-Series (GOES-R) were launched on November 19, 2016 and March 1, 2018 respectively. GOES-16 officially became GOES East on December 18, 2017, and the designation of GOES-17 as GOES West occurred on February 12 2019. The Advanced Baseline Imager (ABI) is the primary instrument on GOES-16 and GOES-17 for imaging Earths surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. The Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess INR performance of GOES-R series ABI images. In this paper, we first describe the assessment of IPATS algorithm accuracy. Next, we present the relationship between view zenith angle (VZA) and the quality of the IPATS measurements. Lastly, we present GOES-16 and GOES-17 navigation (NAV) assessments results from flight data spanning from the start of INR assessment to June 2019. The results show a) IPATS stair step measurement error is less or equal to 0.06 ABI pixel with IPATS baseline configuration; b) VZA is an effective filter to exclude outliers of the measurements; and c) ABI INR for both satellites has improved over time as post-launch tests (PLT) were performed and corrections applied. This paper also shows that the post-launch INR tuning of GOES-17 was much shorter than GOES-16.

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Description: NASA's legacy Satellite Laser Ranging (SLR) network produces about one-third of the global SLR data to support spacegeodesy. This network of globally distributed stations has been using Time Interval Units (TIU) for range measurements for thelast 25 + years. To improve the reliability of the SLR network and satisfy the need for stable millimeter precision data, a phasedreplacement of the TIUs in the network with picosecond-precise Event Timer Modules was initiated in 2015. This schemeallowed the time of flight and laser transmit epoch measurement to one picosecond resolution. For a network with globalscientific impact, transitioning to a new data generation metrological scheme requires significant data scrutiny and long-termscience data validation. Any long-term testing/measurement has the potential to interrupt the station's daily operational dataflow to the International Laser Ranging Service (ILRS) as the station under test will have to put its test data into quarantine.We have demonstrated a very effective way to test and implement the new device without removing the old hardware andwithout the need for the orbit analysis. This operationally noninvasive scheme performed concurrent test measurements enablinguninterrupted operational data flow to the users, while allowing simultaneous test data capture for short- and long-termsystematics and stability analysis. Extensive analysis of the test data was performed by the NASA SLR engineering team andthe ILRS Analysis Standing Committee, to uncover biases and any dependencies on the satellite ranges (for nonlinear scaleissues). Multi-ETM comparison was also performed at two of the SLR stations through the interchange of hardware to establishthe inter-device range biases and stability. Such benchmarked hardware was subsequently sent to the remaining stationsto allow traceability and normalize the network performance. The range bias intercomparison performed using the multiyearSLR data analysis agreed well with the engineering changes, thus validating the approach to flush out station-specific rangingsystematics affecting precise orbit determination. Such an improvement and rebalancing of the current network will allowan orderly transition of the current NASA SLR network operating at a maximum rate of 10 Hz to the NASA next generationSpace Geodesy Satellite Laser Ranging (SGSLR) network operating at 2 kHz (McGarry et al. in J Geod, 2018. https ://doi.org/10.1007/s0019 0-018-1191-6; Merkowitz et al. in J Geod, 2018. https ://doi.org/10.1007/s0019 0-018-1204-5).

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Description: Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2018, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to June 28, 2019, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2018 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2018, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to June 28, 2019, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2018, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to June 22, 2019, at 30.4375-day intervals.

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Description: This seminar focuses on its applications to geoprocessing, leaving aside its applications to cartography.

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Description: Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2017, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to June 29, 2018m at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2017 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2017, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962 to June 29, 2018, at daily intervals and which are also available inversions with epochs given at either midnight or noon; and (2) POLE2017, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1990, to June 22, 2018, at 30.4375-day intervals.

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Description: An important aspect to using imaging spectrometer data is the radiometric characterization and calibration of the sensors and validation of their data products and doing so with error budgets with known traceability. The radiometric accuracy of a given sensor is important for demonstrating the expected quality of data from the sensor. Known traceability allows data from multiple sensors to be directly comparable as will become more important in the near future with the expected launches of multiple imaging spectrometers from multiple countries, agencies, and commercial entities. The current work describes the state of pre- and post-launch radiometric absolute and relative uncertainties and their role in harmonising on-orbit data. Examples of prelaunch uncertainties based on the calibration of EnMAP and the calibration planned for the CLARREO Pathfinder Mission are presented highlighting recent work in the area of detector-based approaches using tunable laser sources. Post-launch calibration approaches for Pathfinder, EnMAP, CHIME, and DESIS including traditional vicarious calibration methods and the challenges of working with commercial data are presented. The vicarious calibration discussion relies on the example of the recently-available RadCalNet data to describe typical methods and challenges that will be faced when harmonising data between imaging spectrometers as well as with multispectral sensors.

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Description: The Goddard Earth Sciences Data Information and Services Center (GES DISC) archives and distributes a number of observational and model carbon cycle science data sets. We also provide services that facilitate data discovery, intercomparison, and visualization of these heterogeneous datasets for both research and applications users, such as subsetting, format conversion, How-To documentation, and the Help Desk.

Description: For some time, volcanic eruptions have been thought to be the only significant direct injectors of aerosols in the stratosphere. However, recent fire seasons have featured fire events resulting in large volcanic-sized pyrocumulonimbus plumes of smoke aerosols reaching many kilometers into the lower stratosphere. To understand and model the effects of these pyrocumulonimbus events on stratospheric composition and climate, a natural analogy lies with better studied volcanic events; however, differences in plume composition may limit this comparison. Using satellite lidar from both CATS and CALIOP, we show that the stratospheric aerosol plumes from the record-setting Pacific Northwest pyrocumulonimbus event of 2017 and the Calbuco volcanic eruption of 2015 evolve differently both vertically and microphysically. Specifically, depolarization ratios indicate that this pyrocumulonimbus events aerosol particles became more irregularly shaped over time in contrast to volcanic aerosols which become more spherical over time. Accounting for these changes in aerosol properties may be significant in assessing the effects of pyrocumulonimbus events on the Earths radiative balance and aid in refining stratospheric aerosol typing algorithms to differentiate volcanic from pyrocumulonimbus plumes.

Description: Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the full suite of multi-spectral information content. These combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications. In initial versions of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of DPR sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with a surface emissivity and non-raining water vapor retrieval using the passive observations are explored. Over both ocean and land, the surface retrieval is used to identify areas with high probability of light precipitation and drizzle which is then quantified using techniques derived from the higher sensitivity CloudSat mission. Results indicate successful inclusion of drizzle in the retrievals that can then be included in the constellation databases, as well as improvement in passive microwave false positive precipitation signals over land in cases where surface scattering was misinterpreted as precipitation signal. The inclusion of the dynamic surface information also creates a more robust, radiometrically consistent retrieval scheme for process studies and hydrologic applications.

Description: The future trajectory of the stratospheric ozone recovery will be sensitive to greenhouse gas concentrations through thermal control of chemical loss and via stratospheric circulation changes. The latter in particular is subject to considerable uncertainty meriting continuing monitoring of the evolution of ozone throughout the depth of the stratosphere. Atmospheric reanalyses utilize the data assimilation methodology to obtain comprehensive representations of the state of the atmosphere, including its composition, on multidecadal scales by combining diverse measurements from satellite-borne and conventional data sources. Systematic biases among these various data types pose a challenge for assimilation by introducing spurious discontinuities that affect the utility of reanalyses for studies of long-term variability and trends.In this presentation we will outline an approach, developed at NASA's Global Modeling and Assimilation Office (GMAO), that allows joint assimilation of stratospheric ozone profiles from the Microwave Limb Sounder (MLS) on EOS Aura and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) currently flying on the Suomi-NPP satellite with future missions projected into the 2030s. We will demonstrate that a simple offline correction significantly reduces biases between MLS and OMPS-LP ozone data providing a strategy for generating a long-term vertically resolved homogenized representation of stratospheric ozone in future reanalyses. One novel element of our approach compared to previous GMAO reanalysis is the use of a version of the Goddard Earth Observing System model with full stratospheric chemistry. We will show selected comparisons of MLS and OMPS-LP assimilation experiments with independent ozonesonde and satellite data as well as two examples of process-based evaluation focused on the 2016 QBO disruption and Arctic winter ozone loss focusing on the relative performance of the MLS and OMPS-LP analyses.

Description: The Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3, launched in 2015 and 2019, respectively, are intended to collect and deliver high-resolution observations of CO2 with unprecedented space and time coverage. Observations of CO2 from these remote-sensing missions (also known as XCO2, or column-based average, dry air mole fraction of CO2) are then used by the global carbon cycle community to answer a wide range of science questions, from the distribution and quantification of global and regional CO2 source-sink patterns to quantification of anthropogenic sources at urban scales. Even though we have had the OCO-2 mission flying for a few years now, the retrieval algorithms are continuously evolving and improving to deliver XCO2 retrievals with very high precision and high accuracy (or low biases). In this presentation, we will discuss a simple yet effective quantitative framework that has been developed by the OCO-2 flux team to evaluate the information content of these XCO2 retrievals as soon as they are released, i.e., with lower latency than full-scale flux inversions. This framework serves as a precursor to advanced inverse modeling frameworks and is intended to provide an early but accurate assessment of the signal present in the satellite retrievals, the robustness of that signal, and the ability of these retrievals to resolve patterns in CO2 surface fluxes that cannot be resolved by our current network of surface sites. Specific results will tackle a tiered set of questions that are being addressed using this framework: (a) what are the distribution of retrievals in the different modes of operation and how do they vary in space and time? (b) what is the information that is being given to the inverse modeling frameworks from the space-based data, information above and beyond what is provided by the in-situ data? and (c) how do these factors influence our choices for doing flux inversions with the satellite retrievals? While the primary focus of the results will be on application of this technique to mature OCO-2 retrievals, we will show early results for a couple of months of OCO-3 retrievals. For the time-period that the retrievals from the two missions overlap, we will highlight how this framework allows us to effortlessly put the information from OCO-3 and OCO-2 on an equal footing, thus enabling easy comparison between the two pioneering missions.

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Description: Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Description: Version 2 of the coupled modeling and analysis system used to produce near real time subseasonal to seasonal forecasts was released almost two years ago by the NASA/Goddard Global Modeling and Assimilation Office. The model runs at approximately 1/2 degree globally in the atmosphere and ocean, contains a realistic description of the cryosphere, and includes an interactive aerosol model. The data assimilation used to produce initial conditions is weakly coupled, in which the atmosphere-only assimilated state is coupled to an ocean data assimilation system using a Local Ensemble Transform Kalman Filter. Results of aerosol-derived air quality (Particulate Matter) from an extensive series of retrospective forecasts will be shown, with particular focus on the continental United States and eastern Asia. In addition, under some circumstances, the interactive aerosol is shown to improve seasonal time scale prediction skill. Plans for a future version of the system with predicted biomass burning from fires will also be discussed.

Description: Satellite earth observation of aerosols using a single sensor have been done for more than three decades. The retrieved global distribution of column integrated aerosol optical depth (AOD) has been refined with higher accuracy and finer resolutions. However, the other aerosol properties, such as aerosol absorption and aerosol height, haven not been mapped as extensively using passive sensors. The detectors sensitive to these properties (UV-based sensors such as Aura-OMI) lack the adequate visible channels to tease out the absorption, height and loading contributions. In addition, their spatial resolution is prone to frequent cloud contamination impacting aerosol loading retrievals using only these spectral ranges. However, the deployment of the VIIRS and OMPS-NM sensors on the same Suomi NPP platform provides a first opportunity to carry out retrievals of aerosol single scattering albedo (SSA) and AOD with little cloud contamination using an expanded spectral range from the UV to the near-IR wavelengths. After collocated VIIRS pixel within the large OMPS footprint during the high resolution OMPS observing mode, we compared the radiance from OMPS and VIIRS to ensure the calibration from individual sensors are comparable. Then a look up table (LUT) was created for a smoke case over central Africa. The LUT is generated using 10 channels from 0.357 to 2.1 micron with 44 combinations of aerosol models and 4 different aerosol heights. With CALIOP and AERONET observation nearby, the possibility of using OMPS and VIIRS radiance as a single virtual sensor to simultaneously and consistently retrieve AOD, SSA, and aerosol height is explored.

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Description: NASA report on Calibration/Validation activities, including recent launches of ICESat-2, GEDI and OCO-3.

Description: The Climate Absolute Refractivity and Reflectance Observatory (CLARREO) Pathfinder (CPF) mission is being developed to demonstrate SI-traceable retrievals of reflectance at unprecedented accuracies for global satellite observations. An Independent Calibration of the CPF sensor using the Goddard Laser for Absolute Measurement of Radiance (GLAMR) is planned to allow validation of CPF accuracies. GLAMR is a detector-based calibration system relies on a set of NIST-calibrated transfer radiometers to assess the spectral radiance from the GLAMR sphere source to better than 0.3 % (k=2). The current work describes the calibration of the Solar, Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS) that was originally developed as a calibration demonstration system for the CLARREO mission and is now being used to assess the independent calibration being developed for CPF. The methodology for the radiometric calibration of SOLARIS is presented as well as results from the GLAMR-based calibration of SOLARIS. The portability of SOLARIS makes it capable of collecting field measurements of earth scenes and direct solar and lunar irradiance similar to those expected during the on-orbit operation of the CPF sensor. Results of SOLARIS field measurements are presented. The use of SOLARIS in this effort also allows the testing protocols for GLAMR to be improved and the field measurements by SOLARIS build confidence in the error budget for GLAMR calibrations. Results are compared to accepted solar irradiance models to demonstrate accuracy values giving confidence in the error budget for the CLARREO reflectance retrieval.

Description: For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles.The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview interactive mapping site provide a unique view into EOS data through daily full resolution visualizations of hundreds of Earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.This presentation will provide an overview of the visualizations available in NASA GIBS and Worldview and how they are accessed. Specific attention will be given to the newer capabilities and accomplishments, including: Support for geostationary sub-daily visualizations, Enhanced support for vector-based visualizations, Improved Worldview tour and snapshot capabilities, New imagery products across a growing set of scientific areas.

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Description: Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratorys Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018.

Description: Being developed in partnership between NASA and the Indian Space Research Organisation (ISRO), the NASA-ISRO Synthetic Aperture Radar (NISAR) satellite is planned to launch in late 2020. NISAR will measure many aspects of how Earth is changing with unprecedented accuracy on a global scale from a Low Earth Orbit (LEO) platform. With a 12m deployable mesh reflector, NISAR will feature one of the largest deployable mesh reflector ever launched for a scientific mission. Two large planar phased arrays will feed the reflector, one that will operate at L-Band and be developed by the Jet Propulsion Laboratory (JPL), and an S-band array that will be developed at the ISRO Space Application Centre (SAC). This paper describes the preliminary design of the L-Band feed array.

Description: Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual-polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Description: Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Description: NASA's EOS Data and Operations System (EDOS) is the primary supplier of NRT (near real-time) data to the NASA NRT user community known as the Land, Atmosphere NRT Capability for EOS (LANCE). EDOS provides NRT data for various instruments on the EOS missions Terra, Aqua, Aura, as well as for the NOAA missions Suomi NPP and NOAA-20. This poster describes an overview of the EDOS multi-mission system with emphasis on the NRT products distributed for LANCE elements: AIRS, MISR, MLS, MODIS, MOPITT, OMPS, OMI and VIIRS. Remote EDOS high-rate data capture systems are deployed at NASA ground stations which provide data-driven capture of high-rate science for EOS missions. The remote EDOS components transfer the science data via high-rate WANs to the centralized EDOS Level-zero processing systems located at Goddard Space Flight Center. EDOS produces session-based data sets especially for LANCE NRT use from a single ground station contact; this data is sent to dual LANCE destinations as part of the standard redundancy requirement for LANCE elements. EDOS has implemented various latency improvements with the ultimate goal to have EDOS processing of NRT data keep up with the spacecraft data downlink. EDOS enhancements have included implementation of priority-based QoS, expanded network architecture to include open networks, and use of a delay-tolerant protocol. EDOS has streamlined its systems and infrastructure to minimize latency for NRT data delivery for LANCE. EDOS begins to transfer the NRT data to the LANCE elements within minutes of the end of the contact session with an average packet latency from instrument observation to Level 0 product delivery to each LANCE element of just over one hour.

Description: No abstract available

Description: No abstract available

Description: The construction of hydropower dams is a common strategy to support a country's increasing need for electricityand river water management for industry and agriculture. Although the hydrological and geophysical impacts ofwater relocation are usually assessed prior to impoundment, their accuracy is generally limited due to the lack ofin situ observations, especially in a remote area. This study presents a workflow to quantify the terrestrial waterstorage change (TWS) and land subsidence induced by a reservoir's water impoundment using multiple satelliteobservations (GRACE, Landsat), land surface models (CABLE, GLDAS, NCEP, ECMWF), and GPS data. The studysite is the Bakun Dam, located in Sarawak, Malaysia, which is the largest hydropower dam in Southeast Asia.Commencing operation in late 2010, the dam induced a change of water mass and lake surface area that wasclearly observed by GRACE and Landsat observations, respectively. During the 17-month impounding period(from August 2010 to December 2011), GRACE observed a dramatic increase of approximately 200mmequivalent water height, while Landsat detected an increased lake extent of around 600 km2. In this paper, aforward model is developed to determine the increased water surface level corresponding to GRACE observations,estimated to be about 120 m. In contrast to GRACE, the TWS derived from land surface models cannotcapture the increased TWS, due to the lack of reservoir routing algorithms in the models. In addition, the landsubsidence was calculated using the disk load model constructed based on the GRACE-derived lake level andLandsat-derived lake extent; the result is validated with the GPS data from BIN1 station, located at the westerncoast of Borneo. The commencement stage of the Bakun Dam induces the large-scale land subsidence, whichcauses the GPS-BIN1 station to subside by ~9 mm, and move toward the Bakun Lake by ~4 mm. Computation ofthe surface displacements directly from GRACE spherical harmonic coefficient data fails to capture the subsidencefeature, mainly due to the truncation error. Overall, this study demonstrates that evaluating GRACE inconjunction with Landsat, LSMs, and GPS data allows the exploitation of the gravity signal at a much smallerspatial scale than its intrinsic resolution. Benefiting from global coverage, the newly developed satellite-basedalgorithm is a valuable tool for assessing the impacts of reservoir operation on hydrological and geophysicalchanges from local to regional scales.

Description: Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Description: Mangrove forests are found in intertidal zones of tropical regions around the world and provide important ecological and economic benefits they are considered carbon sequesters, habitats for flora and fauna, and natural barriers to hurricanes and tsunamis. Wood from mangrove forests are used as fuel and building materials in surrounding coastal communities, therefore promoting local livelihoods. Despite the importance of these ecosystems, mangrove forests have historically been degraded in natural processes such as severe weather, and anthropogenic factors like conversion to agriculture and aquaculture. This study assesses change in mangrove forests in Nigeria and Mozambique from 2015 to 2018 using SAR and optical data fusion. Due to frequent cloud cover over the study area, SAR and optical data is fused to obtain gap-free imagery without clouds. Landsat-8 OLI and Sentinel-1 imagery is fused with TensorFlow, an open source platform used in developing machine learning models. The resulting images are classified to discriminate mangrove forest cover from other land cover types, and change is estimated using image differencing. Understanding the rates and magnitude of mangrove change across space and time can aid in identifying priority areas for forest regeneration, and can help construct sustainable management practices for the future.

Description: Understanding the needs of the end user is vital to producing quality, usable software that solves real problems. Additionally, making sure those needs are communicated to managers, engineers, and designers at the project level is vital. On complex projects, it's important to build out resources for your team that make it easy to put yourself in the shoes of the specific user you're building for. NASA's Earth Observing System is a collection of data, applications, and a diverse user and scientific community that's trying to answer tough questions about our planet and its climate. Over the last few years, we've spoken to hundreds of users, performed many user testing sessions, and built a collection of user personas, user stories, and design assets that help guide new software and feature development within NASA EOSDIS. We've also developed methods for synthesizing this information and making it actionable for teams, and worked to foster a design first approach on new projects always starting from a core user need and working backward from interface development to software engineering. This process has allowed us to design better, more usable software and features that directly meet the needs of our user community.

Description: No abstract available

Description: No abstract available

Description: Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASAs Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASAs Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

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Description: Dependence on rainfed agriculture in a highly variable climate, renders crop and livestock production vulnerable to impacts of drought in Kenya. Stakeholders in the region have highlighted the need for timely and actionable detailed early warning information on drought and its implication on crop productivity. Here we apply the Regional Hydrological Extremes Assessment System (RHEAS) to estimate current and future drought conditions onset, severity, recovery, and duration) and expected productivity outlooks.

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Description: Along with scientific applications, Geostationary imagery is often used to learn about weather patterns through true color visualizations. NOAA/NASA's GOES-R series of satellites uses the advanced baseline imager with 16-bands which, unlike previous generations, does not include the green wavelength (500-565 nm) and hence cannot directly generate true color images. However, Himawari, Japan's geostationary satellite, uses a similar 16-band advanced Himawari imager that does include a green band (but missing cirrus). In this work, we show how transfer learning with convolutional neural networks can be applied across satellites to generate "virtual sensors". We apply this approach to generate a green band for GOES-16 and present near true color images.

Description: No abstract available

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Description: Value-added imagery products from the GOES-R series Advanced Baseline Imager are used to identify features of interest for operational forecasters, including: blowing dust, smoke, volcanic ash, cloud cover, atmospheric moisture, biomass fires and snow cover. These features each have a specific spectral signal that algorithms are designed to detect, but those signals are not always unique to just a single feature. Sometimes, the surface can mimic the spectral signal associated with an atmospheric feature of interest, creating a false alarm in a value-added product which negatively impacts interpretation of that product. This case study focuses on one such situation where areas of desert in the southwestern United States mimic the positive value associated with low clouds in the Brightness Temperature Difference (BTD) between the 10.35 m and 3.9 m channels, producing a false alarm. In order to characterize these false alarm areas, this study uses the cloud-cleared background product from the Cooperative Institute for Research in the Atmosphere (CIRA) to create a dataset of the cloud-free surface. Previous results have shown that cloud-cleared backgrounds of the 10.35 m-3.9 m BTD have successfully identified areas of positive BTDs in the cloud-free environment, proving that it is a property of the surface emissivity and a true false alarm. This study builds on that previous research to further characterize the seasonality of the false-alarm regions and to catalogue how they impact interpretation of multi-spectral imagery products from the GOES-R series ABI, especially the Nighttime Microphysics RGB created by NASA Short-term Prediction Research and Transition (SPoRT) Center.

Description: The world around us is constantly in motion. Storms swirl, fires rage, volcanoes erupt and icebergs calve. NASAs fleet of Earth Observing System (EOS) satellites are there to capture this. Within hours of satellite overpass, NASAs Worldview (https://worldview.earthdata.nasa.gov) delivers this global, near-real time imagery through an interactive web map application. Provided through NASAs Land Atmosphere Near real-time Capability for EOS (LANCE) (https://earthdata.nasa.gov/lance) via NASAs Global Imagery Browse Services (GIBS) (https://earthdata.nasa.gov/gibs), the near real-time satellite imagery provides a launching point to discover where the latest wildfires, severe storms, volcanic eruptions, and calving ice shelves are happening. This poster will explore the newest near real-time satellite imagery and soon-to-be available imagery in Worldview, including imagery from geostationary satellites - GOES-East/West and Himawari-8. The poster will cover recent and future improvements to Worldview aimed to enhance the discovery and interaction with near real-time imagery and show how it is used by people from researchers, to meteorologists to the science-minded public around the world.

Description: Crop evapotranspiration (ETc) monitoring may support improved irrigation water management in California's Central Coast agricultural region. The CropManage (CM) web-application, developed and operated by the University of California, informs irrigation and nitrogen scheduling decisions made by growers and crop consultants. Daily weather conditions are accounted for by use of grass reference ET data imported from the California Dept. Water Resources. Satellite observations of crop development from NASA's Satellite Irrigation Management Support can be imported and used by CM to adjust crop phenology. A replicated irrigation trial was performed July-October 2018 to investigate yield response of drip-irrigated celery (Apium graveolens) to applied water volume, and to further calibrate CM for use in that crop. Water was uniformly applied by sprinklers to establish the crop, and CM was then used to guide surface drip irrigation treatments applied three times weekly at 50%, 75%, 100%, 125% and 150% of ETc replacement level. Nitrogen fertilizer totaling 380 kg/ha was applied through the drip system generally once per week. Treatments were evaluated for commercial yield 85 and 93 days after transplanting (DAT). The 100% ETc treatment yielded 83 Mg/ha 93 DAT, which was 10 Mg/ha higher than the reported regional average yield in 2017. The 100% treatment received a seasonal total of 34 cm of water, compared with reported grower average exceeding 48 cm. Higher yields of 102 and 108 Mg/ha were observed in the 125% and 150% ETc treatments, respectively. Regarding crop quality, however, the 150% ETc treatment suffered greater incidence of pith breakdown and basal rot than the 100% treatment. Yields from the 50% and 75% treatments were below regional average. Aboveground biomass was evaluated 87 DAT. Fresh and dry biomass accumulations were positively related to applied water volume. Additional findings will be presented for a cauliflower trial conducted spring/summer 2019.

Description: NASA committed to support the collection and distribution of Earth science data to study global change in the 1990's. A series of Earth science remote sensing satellites, the Earth Observing System (EOS), was to be the centerpiece. The concept for the science data system, the EOS Data and Information System (EOSDIS), created new challenges in the data processing of multiple satellite instrument observations for climate research and in the distribution of global-coverage remote sensor products to a large and growing science research community. EOSDIS was conceived to facilitate easy access to EOS science data for a wide heterogeneous national and international community of users. EOSDIS was to provide a spectrum of services designed for research scientists working on NASA focus areas but open to the general public and international science community. EOSDIS would give researchers tools and assistance in searching, selecting and acquiring data, allowing them to focus on Earth science climate research rather than complex product generation. Goals were to promote exchange of data and research results and expedite development of new geophysical algorithms. The system architecture had to accommodate a diversity of data types, data acquisition and product generation operations, data access requirements and different centers of science discipline expertise. Steps were taken early to make EOSDIS flexible by distributing responsibility for basic services. Many of the system operations concept decisions made in the 90s continued to this day. Once implemented, concepts such as the EOSDIS data model played a critical role developing effective data services, now a hallmark of EOSDIS. In other cases, EOSDIS architecture has evolved to enable more efficient operations, taking advantage of new technology and thereby shifting more resources on data services and less on operating and maintaining infrastructure. In looking to the future, EOSDIS may be able to take advantage of commercial compute environments for infrastructure and further enable large scale climate research. In this presentation, we will discuss key EOSDIS operations concepts from the 1990's, how they were implemented and evolved in the architecture, and look at concepts and architectural challenges for EOSDIS operations utilizing commercial cloud services.

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Description: No abstract available