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NASA NTRS2018

Science Diplomacy Through Cities: Applying NASA Earth Observations at the Urban Scale

NASA's scientific expertise and data products are enhancing cities' environmental monitoring activities by pioneering applications of remote sensing and model-based Earth Observations at the urban scale. The above activities have greatly benefitted from engaging stakeholders and city practitioners from the start. Further, NASA's collaborations with cities have: Advanced NASA science, in testing new products and validating of satellite datasets, while meeting the needs of city governments. Broadened Rio de Janeiro's regional viewpoint and strengthened its relationships with neighboring cities. Scientific collaborations with cities benefit from: Selecting city partners with a high level of technical capacity and willing to make strong investments in joint projects. Sustained communication and face-to-face interactions. Well-defined deliverables, with dedicated resources and personnel. Pairing global datasets and projections with in situ measurements and local knowledgeSensitivity to local working culture and politics.

urban environment↗

NASA NTRS2018

GEONEX: Webpage to Display NASA-NOAA Collaboration of Producing Land Surface Products from Geostationary Sensors

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.In order to better introduce the GEONEX products to the science community, we set up a simple webpage (www.geonex.org) to describe the background and the motivation of the project, the algorithms used in deriving the products, and user manuals to the data files. We will also update the status of the data processing, in particular the near-real-time products, on the website and provide links (in text or json files) to the latest datasets.

geostationary↗

NASA NTRS2018

GeneLab: "Omics" Data System for Space Biology Research

Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA's GeneLab Data Systems (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an overview of GeneLab and examples of analysis that are being done with GeneLab datasets. These example will include novel data and work being generated with various scientists around the world involved with GeneLab's Analysis Working Groups (AWG) that are assisting with the development of pipelines and advancing GeneLab to the next phase, a publication from GeneLab discovering novel Carbon Dioxide impact due to rodent habitats, another publication from GeneLab discovering a potential master regulator responsible for health risk associated due to spaceflight, and potential cardiovascular risk from space radiation.

microgravity↗

NASA NTRS2018

Increasing the Statistical Rigor of Cross-Species Differential Expression Analysis

Microgravity inflicts substantial, but undercharacterized, pressure on organisms that induces metabolic responses such as increased microbial virulence and antibiotic resistance, altered organ weights in developing rats, and loss of bone tissue in astronauts. Numerous studies have analyzed the effects of microgravity on specific organisms, tissues, or test conditions, but these projects are necessarily limited by the small sample size of space research. Increasing the sample size of spaceflight studies is non-trivial; however, pooling data from numerous studies can greatly increase the statistical rigor of comparative analyses. The GeneLab houses datasets from 73 spaceflight studies that performed transcription profiling assays. These data encompass a diverse array of organisms ranging from Escherichia coli to Mus musculus to Homo sapiens and comprise studies analyzing ionizing radiation, mammalian pregnancy, etc. Collectively, the GeneLab database contains a large quantity of transcription assays and RNA sequence data analyzing Differential Gene Expression (DGE) between microand normogravity. Xspecies, a cross-species analysis method for DGE developed by Kristiansson, et al. in 2012, identifies homologous genes between species that are universally up- or downregulated in response to test conditions. Previous work by an intern at GeneLab applied Xspecies to 19 datasets containing seven different species and identified 14 homologous groups differentially expressed under spaceflight conditions including several heat shock proteins and cytoskeletal components. Unfortunately, these results may be biased by the disproportionate number of studies on Arabidopsis thaliana (5) and Mus musculus (6) and the results are not normalized by evolutionary distances. Here, we present modifications to the Xspecies algorithm that permits incorporation of multi-omic data and normalizes data for effect size, directionality, and evolutionary distances. We then apply this algorithm to all currently available GeneLab studies

Xspecies↗

NASA NTRS2018

The Land Surface Data Toolkit (LDT v7.2) - A Data Fusion Environment for Land Data Assimilation Systems

The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

droughts and floods↗

NASA NTRS2018

Soil Moisture Active Passive (SMAP) Project Assessment Report for Version 4 of the L4_SM Data Product

This report provides an assessment of Version 4 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product, released on 14 June 2018. The assessment includes comparisons of L4_SM soil moisture and temperature estimates with in situ measurements from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product, including observation-minus-forecast (O-F) brightness temperature residuals and soil moisture analysis increments.Together, the core validation site comparisons and the statistics of the assimilation diagnostics areconsidered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid-cell scale of the data product.The Version 4 L4_SM product benefits from an improved land surface modeling system and from retrospective surface meteorological forcing data that are as consistent as possible with the present-day datain terms of their climatology. Specifically, the model changes include revised parameters and parameterizations for (i) the surface energy balance, (ii) recharge from below of the model's surface excess reservoir, and (iii) the snow depletion curve. Updated ancillary inputs include improved datasets for landcover, topography, and vegetation height. The Version 4 algorithm further includes a revised approach to precipitation corrections that improves the precipitation climatology in Africa and the high-latitudes. Moreover, for system calibration the model is forced retrospectively with MERRA-2 reanalysis data, which are more consistent with the near-real time GEOS forward processing (FP) data used during the SMAP period than the retrospective GEOS data that were available for previous L4_SM versions. An analysis of the time-average surface and root zone soil moisture shows that the global pattern ofarid and humid regions is captured by the Version 4 L4_SM estimates. Owing to the changes in the landsurface modeling system, surface soil moisture is typically drier by several volumetric percent in Version 4 compared to Version 3, whereas root zone soil moisture is wetter in Version 4 in some regions and drierin others. Because of these climatological differences, the Version 3 and Version 4 products should not be combined into a single dataset for use in applications.Results from the core validation site comparisons indicate that Version 4 of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the RMSE after removal of the long-term mean difference (ubRMSE). The overall ubRMSE of the 3-hourly L4_SM dataat the 9 km scale is 0.039 m3 m-3 for surface soil moisture and 0.029 m3 m-3 for root zone soil moisture,below the 0.04 m3 m-3 requirement. The L4_SM estimates are an improvement over estimates from a model-only Nature Run version 7.2 (NRv7.2), which demonstrates the beneficial impact of the SMAP brightness temperature data. Overall, L4_SM surface and root zone soil moisture estimates are more skillful than NRv7.2 estimates, with statistically significant improvements at the 5% level for surface soil moisture R and anomaly R values. Results from comparisons of the L4_SM product to i

Reichle, Rolf H.↗

NASA NTRS2018

GeneLab: "Omics" Data Systems for Spaceflight and Simulated Spaceflight Environment

spaceflight↗

NASA NTRS2018

Overcoming Pitfalls When Using HDF5 Compression

Compression of large datasets in S-NPP, JPSS and other HDF5 data files may substantially reduce their size, in turn reducing disk space requirements and file download time. However, mismatches between the layout of the files' datasets, the HDF5 instance's cache settings, choice of compression algorithm, and the access pattern of applications using the data can sometimes result in poor performance or exhausting machine resources when running the application. Whether designed in advance or modified in response to encountered problems, applications can be tuned to optimize efficiency of data access, avoid unnecessary repeated decompression, and reduce the amount of memory used. Examples will be given of problems that may be encountered, how to use available tools to diagnose or work around them, changing cache settings to conserve memory, and designing access strategies to avoid both performance and memory issues when creating or modifying applications.

performance↗

NASA NTRS2018

Challenges in Development of Online Visualization and Analysis Tools for Satellite Data

Over the years, various online visualization and analysis tools have been developed to facilitate satellite data access and help scientific users around the world to conduct research and develop applications (e.g., data product evaluation, what-if questions, etc.). For those who are new to satellite data products, using them can be a daunting task due to many obstacles in data processing such as data formats, complex data structures, special software packages, unfamiliar terminology, etc., especially when one is not sure whether a dataset is suitable for his/er research project. Even for experienced users, developing software for data processing and analysis can be a costly and time-consuming task. Online visualization tools can overcome many of these difficulties and allow users to focus on scientific questions. For example, Giovanni (the Geospatial Interactive Online Visualization and Analysis Infrastructure, https://giovanni.gsfc.nasa.gov), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), allows access over 1900 satellite and model variables in 82 measurement groups of 8 disciplines without downloading data and software. Main features include basic functions for data analysis and visualization, data provenance, output data in different formats (ASCII, NetCDF, GeoTIFF), and more. Over the years, ~1700 peer-reviewed publications in different disciplines have been benefited from Giovanni in research activities (e.g. initial investigation, what-if questions, product evaluation). Despite the success of online visualization and analysis tools, challenges and new opportunities still exist and more can be done with new requirements and technology. Examples are: a) how to increase the efficiency of dataset search by enhancing intuitive aspects; b) how to facilitate interdisciplinary research; c) how to provide data quality information; d) how to engage users to participate in data quality assessment; and more. NASA Earth Observing System Data and Information System (EOSDIS) satellite-based data products are processed at various levels ranging from Level 0 to Level 4. While most users use data products at higher levels (Level-3 and 4), products at lower levels are still important for case studies, algorithm development, ground validation, etc. In this presentation, we will use Giovanni as an example to present and discuss challenges and near-future opportunities for satellite data online visualization and analysis tools.

Liu, Zhong↗

NASA NTRS2018

Satellite-Based View of the Aerosol Spatial and Temporal Variability in the Cordoba Region (Argentina) Using over Ten Years of High-Resolution Data

Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and spatial variability of atmospheric particulate matter (aerosol) over the urban area of Córdoba (central Argentina) using over ten years (2003–2015) of high-resolution (1 km) satellite-based retrievals of aerosol optical depth (AOD). This fine resolution is achieved exploiting the capabilities of a recently developed inversion algorithm (Multiangle implementation of atmospheric correction, MAIAC) applied to the MODIS sensor datasets of the NASA-Terra and -Aqua platforms. Results of this investigation show a clear seasonality of AOD over the investigated area. This is found to be shaped by an intricate superposition of aerosol sources, acting over different spatial scales and affecting the region with different yearly cycles. During late winter and spring (August-October), local as well as near- and long-range transported biomass burning (BB) aerosols enhance the Córdoba aerosol load, and AOD levels reach their maximum values (>0.35 at 0.47 µm). The fine AOD spatial resolution allowed to disclose that, in this period, AOD maxima are found in the rural/agricultural area around the city, reaching up to the city boundaries pinpointing that fires of local and near-range origin play a major role in the AOD enhancement. A reverse spatial AOD gradient is found from December to March, the urban area showing AODs 40–80% higher than in the city surroundings. In fact, during summer, the columnar aerosol load over the Córdoba region is dominated by local (urban and industrial) sources, likely coupled to secondary processes driven by enhanced radiation and mixing effects within a deeper planetary boundary layer (PBL). With the support of modelled AOD data from the Modern-Era Retrospective Analysis for Research and Application (MERRA), we further investigated into the chemical nature of AOD. The results suggest that mineral dust is also an important aerosol component in Córdoba, with maximum impact from November to February. The use of a long-term dataset finally allowed a preliminary assessment of AOD trends over the Córdoba region. For those months in which local sources and secondary processes were found to dominate the AOD (December to March), we found a positive AOD trend in the Córdoba outskirts, mainly in the areas with maximum urbanization/population growth over the investigated decade. Conversely, a negative AOD trend (up to −0.1 per decade) is observed all over the rural area of Córdoba during the BB season, this being attributed to a decrease of fires both at the local and the continental scale.

aerosols↗

NASA NTRS2018

Sea State Based Estimation of White Cap Fraction: Implications for Primary Marine Aerosol Fluxes

Oceanic whitecaps (hereafter, W) or the characteristic whiteness of the sea foam is an important feature for predicting exchange of gases, sea spray aerosols (SSAs), heat and momentum transfer between the ocean and the atmosphere at the air-sea interface. Due to its increased surface emission and brightness temperature, whitecaps are critical for satellite retrievals of ocean albedo, ocean color, ocean surface wind vectors from satellite borne radiometer and microwave instruments. Most of the existing models predict W using wind speed and sea surface temperature (SST). However, numerous publications have pointed out that there are large uncertainties in the predicted W and using parameterizations based on wind-wave state can improve the precision of the predicted W. Here, we integrate the University of Miami Wave Model - 2.0 (UMWM) in Goddard Earth Observing System (GEOS) and use wave diagnostics to predict W. We choose the year 2006 for our global UMWM/GEOS runs because of the availability of W dataset from satellite observations. We run UMWM/GEOS at 0.5o x 0.5o by replaying to MERRA2 meteorology and evaluate the wave diagnostics using measurements from fixed buoys and satellite altimeters. We use three different parameterizations for W based on: 1) Reynolds number, 2) wave dissipation energy, and 3) volume of air entrained by breaking waves. We compare our results of W with previous studies and also with the satellite based observational dataset. Predicting W is important for understanding the processes at the air-sea interface. Therefore, this work is a step further in improving the uncertainties in the aerosol and atmospheric chemistry modules of the global models.

Oceanic Whitecaps↗

NASA NTRS2018

The Importance of User Feedback in Sustaining Trusted Repositories

The NASA Earth Observation System Data and Information System (EOSDIS) has been operating since 1994 and is serving a global user community with well-managed Earth science data in a variety of scientific disciplines. EOSDIS processes, archives and distributes data and information products resulting from spaceborne and airborne instruments as well as in situ measurements from field campaigns. The Earth Science Data and Information System (ESDIS) Project at the NASA Goddard Space Flight Center manages EOSDIS with its 12 Distributed Active Archive Centers (DAACs) located across the United States. During the entire life of EOSDIS, the ESDIS Project and the DAACs have deployed many different mechanisms for user feedback, which have proven extremely valuable to their evolution and performance in their service to user communities. Some of the inputs from our user groups have resulted in fundamental changes in the architecture, design and operations of EOSDIS, while others have provided novel ideas for incremental changes. The EOSDIS DAACs have User Working Groups (UWGs) that represent broad user communities in the Earth science disciplines served by the DAACs. The UWGs meet periodically to assess and provide feedback on dataset and service priorities. As regular users of the data and services of the DAAC and experts in the scientific disciplines, the UWG members provide valuable inputs for planning and prioritizing the services, as well as addition of new datasets, for the benefit of the community. The EOSDIS is evaluated annually through an independently administered survey of its users resulting in the American Customer Satisfaction Index (ACSI). The survey provides an ACSI score as well as free-text suggestions from users, which are also helpful in making specific system improvements. In addition, each of the DAACs has a user services group that address on-going requests for help and other comments from users. The ESDIS Project has established a mechanism through the "earthdata" website (http://earthdata.nasa.gov) for users to provide feedback of any kind and these questions/comments are routed to the appropriate individuals in the Project or the DAACs. These various forms of receiving user feedback and responding to them continue to be extremely valuable in evolving and sustaining our Earth Science repository.

Behnke, Jeanne↗

NASA NTRS2016

Storage of Physical Sample Metadata in the Astrobiology Habitable Environments Database (AHED)

The National Aeronautics and Space Administration has begun an effort to store, curate, and publish information about physical samples collected and analyzed in conjunction with NASA-funded astrobiology research. Astrobiology is a multidisciplinary area of scientific research being conducted by collaborating teams of biologists, chemists, geologists, atmospheric scientists, oceanographers, astrophysicists, astronomers, and other specialists. Astrobiology studies the origin, evolution, and distribution of life in the Universe. NASA uses the results of astrobiology research to focus its future missions on targets of opportunity for the discovery of life off Earth. Astrobiology researchers conduct both field-based and laboratory-based research, during which physical samples are collected, processed, and catalogued. The cataloguing practices employed by different teams of astrobiologists vary widely, and there are no specific standards available to guide the collection and recording of astrobiology sample data. The disparity in data collection approaches and the lack of a centralized sample repository makes it difficult for astrobiology teams to share data and benefit from resultant synergies.To facilitate data sharing within the astrobiology community, NASA is developing a prototype database the Astrobiology Habitable Environments Database (AHED) and an associated set of data collection templates. The database will store information about samples, along with associated measurements and analyses, including information about biological cultures enriched or isolated from samples, and the results of analyses performed on the samples (e.g., via spectrography, microscopy, etc.). In addition, the system will store contextual information about field sites where samples were collected, the instruments or equipment used for analysis, and people and institutions involved in their collection. AHED is being implemented on top of Open Data Repository's Data Publisher [1], an open source software platform for the publication of scientific datasets. The data collection templates under development represent an initial attempt to propose a set of metadata for capture and storage within AHED. The design of these templates is being conducted by a consolidated group of astrobiologists from active research teams at NASA Ames Research Center, assisted by data science and software engineering specialists. These initial templates must be vetted with the broader astrobiology community through a defined process to ensure that they meet community needs. Each template captures a different type of data collection record. For each template, we are developing a list of fields to be captured, including a set of required entry fields, a set of recommended but optional fields, and a set of discretionary fields. A datatype selected from a variety of text and numeric types is specified for each field. Included is a 'choice' type that restricts user input to an enumerated list of values. Many of the fields and field values capture information of particular interest to the astrobiology community, and are intended to facilitate search and retrieval of relevant data across multiple datasets.

Keller, Rich↗

NASA NTRS2018

Investigating Hydrometeorological Conditions Associated with Increasing Dust Events in Southwestern United States

A recent study (Tong et al. 2017) shows a rapid intensification of dust storm activity over the southwestern United States in the past decades. For example, the frequency of windblown dust storms has increased 240 percent from 1990s to 2000s. Increasing dust storms can worsen air quality in the region. The study also finds that the intensification of dust events has a close connection with a fast-rising infectious disease (valley fever) caused by inhaling soil-dwelling fungi (Coccidioides immitis and C. posadasii) in the southwestern United States. Hydrometeorological conditions play an important role in dust storm activity, including winds, precipitation, soil moisture, atmospheric boundary stability, land surface types, etc. In this presentation, we describe our preliminary results of linking the dust trends to variations of hydrometeorological conditions at regional and global scales, using a number of Earth observations, including products from NLDAS (North American Land Data Assimilation System), TRMM (Tropical Rainfall Measuring Mission), and MERRA (Modern Era Retrospective-analysis for Research and Applications) datasets, from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The GES DISC, one of the 12 NASA data centers, is home to multidisciplinary data archives such as precipitation, hydrology, atmospheric chemistry, atmospheric dynamics, etc. We demonstrate that multidisciplinary datasets and services at GES DISC can be used for interdisciplinary investigation to understand the interactions of Earth system components from an observational perspective.

satellite↗

NASA NTRS2018

Enabling Reanalysis Research Using the Collaborative Reanalysis Technical Environment (CREATE)

Modern atmospheric and oceanic reanalysis are valuable assets for atmospheric research and climate monitoring (Kalnay et al. 1996). Now that most reanalysis records are more than 36 years long, the data have become more useful for climate modeling research (Dole et al. 2008). For investigators who need to use multiple reanalysis, a common challenge is that the data are distributed at various sites and often in different formats. The NASA CREATE system provides access to the data in one location in a standard format (one variable per file and standardized metadata in the CMIP5 style; see Table 1 for a list of the key acronyms used in this paper). The collection includes monthly and 6-hourly data from the seven major atmospheric reanalysis: CFSR (Saha et al. 2010), ERA-Interim (Dee et al. 2011), MERRA (Rienecker et al. 2011), MERRA-2 (Gelaro et al. 2017), JRA-25 (Onogi et al. 2007), JRA-55 (Kobayashi et al. 2014), and 20CRv2c (Compo et al. 2011). An ancillary portion of CREATE includes eight ocean reanalysis: NCEP CFSR, CMCC C-GLORSv5 (Storto et al. 2016), ECMWF ORAS4 (Balmaseda et al.2013), ECMWF ORAP5.0 (Zuo et al. 2015), University of Hamburg GECCO2 (Köhl 2015), GFDL ECDA (Zhang et al. 2007), NOAA GODAS (Saha et al. 2010), and MOVE/MRI.COM-G2i (Toyoda et al. 2016). The ocean state variables were similarly reformatted but were then also regridded onto a common horizontal and vertical grid. This approach facilitated the calculation of an ensemble average and spread that is also published alongside the native gridded data. A third reanalysis product is a global hourly 0.5° land surface air temperature dataset constructed by Wang and Zeng (2013). All three datasets are distributed through the ESGF in a format consistent with the CMIP style described by Cinquini et al. (2014).

Potter, Gerald L.↗

NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗

NASA NTRS2019

Satellite Estimates of Momentum Fluxes from High-Impact Gravity Wave Events in the Stratosphere and Their Effects on Circulation

Recent assessments of chemistry-climate models (CCMs) reveal biases in temperatures and winds in, especially but not limited to, the Southern Hemisphere stratosphere, where winds are generally too strong and temperatures too cold. The reasons for these biases are not completely understood, but it is thought that missing wave drag in models is a major culprit. Observational and modeling studies support this idea by elucidating the role of infrequent but very high-impact gravity wave events in the stratosphere. These highly intermittent gravity wave events with large momentum fluxes are the most important drivers of circulation and transport in the stratosphere, yet they are not treated correctly in most global models. This has implications for the cold pole problem in the Southern Hemisphere and the global Brewer-Dobson circulation in general. In this presentation we show results combining HIRDLS and AIRS to derive detailed gravity wave properties and obtain new quantitative estimates of the local and intermittent gravity wave drag in the stratosphere. The combination of high-vertical resolution (1 km) and near-global (60°S to 80°N), close horizontal sampling (100 km) makes HIRDLS temperatures the best available dataset for retrieving gravity wave properties needed to diagnose gravity wave effects on circulation. We further exploit the close zonal sampling of HIRDLS near the turnaround latitude in the Southern Hemisphere to obtain estimates of the missing drag. We combine the HIRDLS results with AIRS brightness temperature images, which reveal high-spatial resolution detail of long vertical wavelength waves, to obtain 3-D, day-to-day variability in gravity wave properties and attribute the wave events to wave sources. The AIRS and HIRDLS datasets complement each other well since the two instruments have very different resolutions and horizontal sampling.

Holt, Laura↗

NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗