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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data

=The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land-atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ~ 1/30 days -1 , 1/30 ~ 1/90 days -1 , and 1/90 ~ 1/365 days -1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time-invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Parameters Describing Earth Observing Remote Sensing Systems

The Earth science community needs to generate consistent and standard definitions for spatial, spectral, radiometric, and geometric properties describing passive electro-optical Earth observing sensors and their products. The parameters used to describe sensors and to describe their products are often confused. In some cases, parameters for a sensor and for its products are identical; in other cases, these parameters vary widely. Sensor parameters are bound by the fundamental performance of a system, while product parameters describe what is available to the end user. Products are often resampled, edge sharpened, pan-sharpened, or compressed, and can differ drastically from the intrinsic data acquired by the sensor. Because detailed sensor performance information may not be readily available to an international science community, standardization of product parameters is of primary performance. Spatial product parameters described include Modulation Transfer Function (MTF), point spread function, line spread function, edge response, stray light, edge sharpening, aliasing, ringing, and compression effects. Spectral product parameters discussed include full width half maximum, ripple, slope edge, and out-of-band rejection. Radiometric product properties discussed include relative and absolute radiometry, noise equivalent spectral radiance, noise equivalent temperature diffenence, and signal-to-noise ratio. Geometric product properties discussed include geopositional accuracy expressed as CE90, LE90, and root mean square error. Correlated properties discussed include such parameters as band-to-band registration, which is both a spectral and a spatial property. In addition, the proliferation of staring and pushbroom sensor architectures requires new parameters to describe artifacts that are different from traditional cross-track system artifacts. A better understanding of how various system parameters affect product performance is also needed to better ascertain the utility of existing datasets and products as well as to specify the performance of new sensors and products. Examples of simulations performed for the Landsat Data Continuity Mission illustrate how various parameters affect system and product performance. Specific examples include the effects of ground sample distance, MTF, and band-to-band registration on various products.

Zanoni, Vicki↗
NASA NTRS2016

Research Objectives for Human Missions in the Proving Ground of Cis-Lunar Space

Beginning in as early as 2023, crewed missions beyond low Earth orbit will begin enabled by the new capabilities of the SLS and Orion vehicles. This will initiate the "Proving Ground" phase of human exploration with Mars as an ultimate destination. The primary goal of the Proving Ground is to demonstrate the capability of suitably long duration spaceflight without need of continuous support from Earth, i.e. become Earth Independent. A major component of the Proving Ground phase is to conduct research activities aimed at accomplishing major objectives selected from a wide variety of disciplines including but not limited to: Astronomy, Heliophysics, Fundamental Physics, Planetary Science, Earth Science, Human Systems, Fundamental Space Biology, Microgravity, and In A major component of the Proving Ground phase is to conduct research activities aimed at accomplishing major objectives selected from a wide variety of disciplines including but not limited to: Astronomy, Heliophysics, Fundamental Physics, Planetary Science, Earth Science, Human Systems, Fundamental Space Biology, Microgravity, and In Situ Resource Utilization. Mapping and prioritizing the most important objectives from these disciplines will provide a strong foundation for establishing the architecture to be utilized in the Proving Ground.

Niles, P. B.↗
NASA NTRS2006

Data Integration Support for Data Served in the OPeNDAP and OGC Environments

NASA is coordinating a technology development project to construct a gateway between system components built upon the Open-source Project for a Network Data AcceSs Protocol (OPeNDAP) and those made available made available via interfaces specified by the Open Geospatial Consortium (OGC). This project is funded though the Advanced Collaborative Connections for Earth-Sun System Science (ACCESS) Program and is a NASA contribution to the Committee on Earth Satellites (CEOS) Working Group on Information Systems and Services (WGISS). The motivation for the project is the set of data integration needs that have been expressed by the Coordinated Enhanced Observing Period (CEOP), an international program that is addressing the study of the global water cycle. CEOP is assembling a large collection in situ and satellite data and mode1 results from a wide variety of sources covering 35 sites around the globe. The data are provided by systems based on either the OPeNDAP or OGC protocols but the research community desires access to the full range of data and associated services from a single client. This presentation will discuss the current status of the OPeNDAP/OGC Gateway Project. The project is building upon an early prototype that illustrated the feasibility of such a gateway and which was demonstrated to the CEOP science community. In its first year as an ACCESS project, the effort has been has focused on the design of the catalog and data services that will be provided by the gateway and the mappings between the metadata and services provided in the two environments.

McDonald, Kenneth R.↗
NASA NTRS2019

Standards and Best Practices - Two NASA Examples

Formal international standards as well as promotion of community or recommended practices have their place in ensuring "FAIRness" of data. Data management in NASA's Earth Observation System Data and Information System (EOSDIS) has benefited from both of these avenues to a significant extent. The purpose of this paper is to present one example of each of these, which promote (re)usability. The first is an ISO standard for specifying preservation content from Earth observation missions. The work on this started in 2011, informally within the Earth Science Information Partners (ESIP) in the US, while the European Space Agency (ESA) was leading an effort on Long-Term Data Preservation (LTDP). Resulting from the ESIP discussions was NASA's Preservation Content Specification, which was applied in 2012 as a requirement for NASA's new missions. ESA's Preserved Data Set Content (PDSC) document was codified into a document adopted by the Committee on Earth Observation Satellites (CEOS). It was recognized that it would be useful to combine PCS and PDSC into an ISO standard to ensure consistency in data preservation on a broader international scale. This standard, numbered ISO 19165-2 has been under development since mid-2017. The second is an example of developing recommendations for "best practices" within more limited (still fairly broad) communities. A Data Product Developers' Guide (DPDG) is currently being developed by one of NASA's Earth Science Data System Working Groups (ESDSWGs). It is for use by developers of products to be derived from Earth observation data to improve product (re)usability. One of the challenges in developing the guide is the fact that there are already many applicable standards and guides. The relevant information needs to be selected and expressed in a succinct manner, with appropriate pointers to references. The DPDG aims to compile the most applicable parts of earlier guides into a single document outlining the typical development process for Earth Science data products. Standards and best practices formally endorsed by the Earth Science Data and Information System (ESDIS) Standards Office (ESO), outputs from ESDSWGs (e.g., Dataset Interoperability Working Group, and Data Quality Working Group), and recommendations from Distributed Active Archive Centers and data producers are emphasized.

Ramapriyan, Hampapuram↗
NASA NTRS2017

Framework for Processing Citizens Science Data for Applications to NASA Earth Science Missions

Citizen science (or crowdsourcing) has drawn much high-level recent and ongoing interest and support. It is poised to be applied, beyond the by-now fairly familiar use of, e.g., Twitter for natural hazards monitoring, to science research, such as augmenting the validation of NASA earth science mission data. This interest and support is seen in the 2014 National Plan for Civil Earth Observations, the 2015 White House forum on citizen science and crowdsourcing, the ongoing Senate Bill 2013 (Crowdsourcing and Citizen Science Act of 2015), the recent (August 2016) Open Geospatial Consortium (OGC) call for public participation in its newly-established Citizen Science Domain Working Group, and NASA's initiation of a new Citizen Science for Earth Systems Program (along with its first citizen science-focused solicitation for proposals). Over the past several years, we have been exploring the feasibility of extracting from the Twitter data stream useful information for application to NASA precipitation research, with both "passive" and "active" participation by the twitterers. The Twitter database, which recently passed its tenth anniversary, is potentially a rich source of real-time and historical global information for science applications. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. Mining the Twitter stream could augment these validation programs and, potentially, help tune existing algorithms. Our ongoing work, though exploratory, has resulted in key components for processing and managing tweets, including the capabilities to filter the Twitter stream in real time, to extract location information, to filter for exact phrases, and to plot tweet distributions. The key step is to process the "precipitation" tweets to be compatible with satellite-retrieved precipitation data. These key components for processing and managing "precipitation" tweets (and additional ones to be developed) are not limited to precipitation, nor are they limited to the Twitter social medium. Indeed, to maximize the value of our work for NASA earth science programs, these components should be generalized and be part of an overall framework for processing citizen science data for science research. In this paper, we outline such a framework.

earth science satellite data↗
NASA NTRS1992

NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications, volume 3

This report contains copies of nearly all of the technical papers and viewgraphs presented at the National Space Science Data Center (NSSDC) Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications. This conference served as a broad forum for the discussion of a number of important issues in the field of mass storage systems. Topics include magnetic disk and tape technologies, optical disk and tape, software storage and file management systems, and experiences with the use of a large, distributed storage system. The technical presentations describe, among other things, integrated mass storage systems that are expected to be available commercially. Also included is a series of presentations from Federal Government organizations and research institutions covering their mass storage requirements for the 1990s.

Kobler, Ben↗
NASA NTRS1986

Preliminary system concepts for MODIS - A moderate resolution imaging spectrometer for EOS

Plans are underway at NASA Goddard to develop a moderate-resolution (0.5-1.0-km) imaging spectrometer with 104 spectral bands in the range from 0.4 to 14.2 microns for use as a NASA facility aboard the Space Station Earth Observation System. Science requirements call for the system to image the earth's surface every 2 d for a period of 10 yr, using two electrooptic sensors. MODIS-T (tilt) has 64 10-nm-wide channels in the 400-1000-nm range. The system has 5-cm-diameter optics, scans + or - 45 deg, and can be pointed + or - 60 deg along track. Calculated S/N is in excess of 800:1 near 400 nm. MODIS-N (nadir) scans + or - 45 deg about nadir with 12 500-m- and 28 1000-m-resolution channels, including two polarization channels and 14 thermal-IR channels. With 20-cm-diameter optics the calculated S/N of the majority of the reflected solar bands is over 1000:1. NETD values for the thermal bands are on the order of 0.1-0.2 K at 300 K. Combined MODIS-T and MODIS-N data rates are 8.3 Mb/s in daylight and 1.5 Mb/s at night, resulting in a total of 360 Gb/d.

Barnes, W. L.↗
NASA NTRS2018

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗
NASA NTRS1991

Payload advisory panel recommendations

The Payload Advisory Panel proposes a restructured Earth Observing System (EOS) mission to address high-priority science and environmental policy issues in Earth System Science. These issues have been identified through studies conducted by the Intergovernmental Panel on Climate Change (IPCC), the United States Environmental Protection Agency (EPA), and the Committee on Earth and Environmental Sciences (CEES). The restructured EOS defers efforts to improve the understanding of the middle and upper stratosphere and solid earth geophysics. The strategy of the mission combines high priority new measurements with continuation of critical data sets begun by missions which precede EOS. Collaborative arrangements with international partners are an essential part of the program and additional arrangements are posed. The need for continuity in Earth observations and the urgency of environmental questions require launch of some EOS elements as soon as possible. They further require maintenance of the EOS objective of obtaining consistent 15-year measurement records.

Moore, Berrien, III↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Urbanization exacerbates continental- to regional-scale warming

Urbanization is usually ignored when estimating past changes in large-scale climate and for future climate projections since cities historically covered a small fraction of the Earth’s surface. Here, by combining global land surface temperature observations with historical estimates of urban area, we demonstrate that the urban contribution to continental- to regional-scale warming has become non-negligible, especially for rapidly urbanizing regions and countries in Asia. Consequently, expected urban expansion over the next century suggests further increased urban influence on large-scale surface climate in the future (approximately 0.16 K for North America and Europe for high-emission scenario in 2100). Based on these results, also seen for air temperature, we argue that, in line with other forms of land use/land cover change, urbanization should be explicitly included in climate change assessments. This requires incorporation of dynamic urban extent and biophysics in current-generation Earth system models to quantify potential urban feedback on the climate system across scales.

Chakraborty, TC↗
NASA NTRS1996

Analysis of the access patterns at GSFC distributed active archive center

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been operational for more than two years. Its mission is to support existing and pre Earth Observing System (EOS) Earth science datasets, facilitate the scientific research, and test Earth Observing System Data and Information System (EOSDIS) concepts. Over 550,000 files and documents have been archived, and more than six Terabytes have been distributed to the scientific community. Information about user request and file access patterns, and their impact on system loading, is needed to optimize current operations and to plan for future archives. To facilitate the management of daily activities, the GSFC DAAC has developed a data base system to track correspondence, requests, ingestion and distribution. In addition, several log files which record transactions on Unitree are maintained and periodically examined. This study identifies some of the users' requests and file access patterns at the GSFC DAAC during 1995. The analysis is limited to the subset of orders for which the data files are under the control of the Hierarchical Storage Management (HSM) Unitree. The results show that most of the data volume ordered was for two data products. The volume was also mostly made up of level 3 and 4 data and most of the volume was distributed on 8 mm and 4 mm tapes. In addition, most of the volume ordered was for deliveries in North America although there was a significant world-wide use. There was a wide range of request sizes in terms of volume and number of files ordered. On an average 78.6 files were ordered per request. Using the data managed by Unitree, several caching algorithms have been evaluated for both hit rate and the overhead ('cost') associated with the movement of data from near-line devices to disks. The algorithm called LRU/2 bin was found to be the best for this workload, but the STbin algorithm also worked well.

Johnson, Theodore↗
NASA NTRSDate not supplied

CERES ERBE-like Monthly Geographical Averages (ES-4) in HDF (CER_ES4_Terra-FM2_Edition1)

The ERBE-like Monthly Geographical Averages (ES-4) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The ES-4 is also produced for combinations of scanner instruments. For each observed 2.5-degree spatial region, the daily average, the hourly average over the month, and the overall monthly average of shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-9 product are spatially nested up from 2.5-degree regions to 5- and 10-degree regions, to 2.5-, 5-, and 10-degree zonal averages, and to global monthly averages. For each nested area, the albedo and net flux are given. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is 'like' the algorithm used for the Earth Radiation Budget Experiment (ERBE). The following CERES ES4 data sets are currently available: CER_ES4_FM1+FM2_Edition1 CER_ES4_PFM+FM1+FM2_Edition1 CER_ES4_PFM+FM1+FM2_Edition2 CER_ES4_PFM+FM1_Edition1 CER_ES4_PFM+FM2_Edition1 CER_ES4_TRMM-PFM_Edition1 CER_ES4_TRMM-PFM_Edition2 CER_ES4_Terra-FM1_Edition1 CER_ES4_Terra-FM2_Edition1 CER_ES4_FM1+FM2_Edition2 CER_ES4_Terra-FM1_Edition2 CER_ES4_Terra-FM2_Edition2 CER_ES4_Aqua-FM3_Edition1 CER_ES4_Aqua-FM4_Edition1 CER_ES4_FM1+FM2+FM3+FM4_Edition1 CER_ES4_Aqua-FM3_Edition2 CER_ES4_Aqua-FM4_Edition2 CER_ES4_FM1+FM3_Edition2 CER_ES4_FM1+FM4_Edition2 CER_ES4_PFM+FM1_Edition2 CER_ES4_PFM+FM2_Edition2 CER_ES4_Aqua-FM3_Edition1-CV CER_ES4_Aqua-FM4_Edition1-CV CER_ES4_Terra-FM1_Edition1-CV CER_ES4_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2005-10-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=2.5 degree; Longitude_Resolution=2.5 degree; Horizontal_Resolution_Range=250 km - < 500 km or approximately 2.5 degrees - < 5.0 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

EOSDIS↗
NASA NTRSDate not supplied

CERES ERBE-like Monthly Regional Averages (ES-9) in HDF (CER_ES9_PFM+FM1+FM2_Edition1)

The ERBE-like Monthly Regional Averages (ES-9) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The ES-9 is also produced for combinations of scanner instruments. All instantaneous shortwave and longwave fluxes at the Top-of-the-Atmosphere (TOA) from the CERES ES-8 product for a month are sorted by 2.5-degree spatial regions, by day number, and by the local hour of observation. The mean of the instantaneous fluxes for a given region-day-hour bin is determined and recorded on the ES-9 along with other flux statistics and scene information. For each region, the daily average flux is estimated from an algorithm that uses the available hourly data, scene identification data, and diurnal models. This algorithm is 'like' the algorithm used for the Earth Radiation Budget Experiment (ERBE). The ES-9 also contains hourly average fluxes for the month and an overall monthly average for each region. These average fluxes are given for both clear-sky and total-sky scenes. The following CERES ES9 data sets are currently available: CER_ES9_FM1+FM2_Edition1 CER_ES9_PFM+FM1+FM2_Edition1 CER_ES9_PFM+FM1+FM2_Edition2 CER_ES9_PFM+FM1_Edition1 CER_ES9_PFM+FM2_Edition1 CER_ES9_PFM+FM1_Edition2 CER_ES9_PFM+FM2_Edition2 CER_ES9_TRMM-PFM_Edition1 CER_ES9_TRMM-PFM_Edition2 CER_ES9_Terra-FM1_Edition1 CER_ES9_Terra-FM2_Edition1 CER_ES9_FM1+FM2_Edition2 CER_ES9_Terra-FM1_Edition2 CER_ES9_Terra-FM2_Edition2 CER_ES9_Aqua-FM3_Edition1 CER_ES9_Aqua-FM4_Edition1 CER_ES9_FM1+FM2+FM3+FM4_Edition1 CER_ES9_Aqua-FM3_Edition2 CER_ES9_Aqua-FM4_Edition2 CER_ES9_FM1+FM3_Edition2 CER_ES9_FM1+FM4_Edition2 CER_ES9_Aqua-FM3_Edition1-CV CER_ES9_Aqua-FM4_Edition1-CV CER_ES9_Terra-FM1_Edition1-CV CER_ES9_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2000-03-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=2.5 degree; Longitude_Resolution=2.5 degree; Horizontal_Resolution_Range=250 km - < 500 km or approximately 2.5 degrees - < 5.0 degrees; Temporal_Resolution=hourly, daily, monthly; Temporal_Resolution_Range=Hourly - < Daily, Daily - < Weekly, Monthly - < Annual].

EOSDIS↗