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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

SRT Evaluation of AIRS Version-6.02 and Version-6.02 AIRS Only (6.02 AO) Products

Version-6 contains a number of significant improvements over Version-5. This report compares Version-6 products resulting from the advances listed below to those from Version-5. 1. Improved methodology to determine skin temperature (T(sub s)) and spectral emissivity (Epsilon(sub v)). 2. Use of Neural-net start-up state. 3. Improvements which decrease the spurious negative Version-5 trend in tropospheric temperatures. 4. Improved QC methodology. Version-6 uses separate QC thresholds optimized for Data Assimilation (QC=0) and Climate applications (QC=0,1) respectively. 5. Channel-by-channel clear-column radiances R-hat(sub tau) QC flags. 6. Improved cloud parameter retrieval algorithm. 7. Improved OLR RTA. Our evaluation compared V6.02 and V6.02 AIRS Only (V6.02 AO) Quality Controlled products with those of Version-5.0. In particular we evaluated surface skin temperature T(sub s), surface spectral emissivity Epsilon(sub v), temperature profile T(p), water vapor profile q(p), OLR, OLR(sub CLR), effective cloud fraction alpha-Epsilon, and cloud cleared radiances R-hat(sub tau) . We conducted two types of evaluations. The first compared results on 7 focus days to collocated ECMWF truth. The seven focus days are: September 6, 2002; January 25, 2003; September 29, 2004; August 5, 2005; February 24, 2007; August 10, 2007; and May 30, 2010. In these evaluations, we show results for T(sub s), Epsilon(sub v), T(p), and q(p) in terms of yields, and RMS differences and biases with regard to ECMWF. We also show yield trends as well as bias trends of these quantities relative to ECMWF truth. We also show yields and accuracy of channel by channel QC d values of R-hat(sub tau) for V6.02 and V6.02 AO. Version-5 did not contain channel by channel QC d values of R-hat(sub tau). In the second type of evaluation, we compared V6.03 monthly mean Level-3 products to those of Version-5.0, for four different months: January, April, July, and October; in 3 different years 2003, 2007, and 2011. In particular, we compared V6.03 and V5.0 trends of T(p), q(p), alpha-Epsilon, OLR, and OLR(sub CLR) computed based on results for these 12 time periods

Susskind, Joel↗
NASA NTRS1987

Mass transport by Aeolian saltation on Earth, Mars and Venus: The effects of full saltation cloud development and choking

Wind tunnel tests were conducted to study the characteristics of particle motion and the quantity transported by wind under a variety of planetary environmental conditions. Analysis of the results indicates that, on Earth, saltation fully develops at near-threshold wind speeds and rarely chokes (retarding of saltation due to high particle concentration and subsequent increase of mid-air collisions). On Mars, saltation becomes fully developed only at favorable locations and at wind speeds well above threshold. The Martian saltation cloud has an insufficient particle concentration to cause choking, particularly over a partially rocky surface. On Venus, full saltation occurs at wind speeds very near threshold, but saltation flux increases less rapidly than expected at relatively modest wind speeds due to choking.

Williams, S. H.↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS1999

A New Normalized Difference Cloud Retrieval Technique Applied to Landsat Radiances Over the Oklahoma ARM Site

We suggest a new approach to cloud retrieval, using a normalized difference of nadir reflectivities (NDNR) constructed from a non-absorbing and absorbing (with respect to liquid water) wavelength. Using Monte Carlo simulations we show that this quantity has the potential of removing first order scattering effects caused by cloud side illumination and shadowing at oblique Sun angles. Application of the technique to TM (Thematic Mapper) radiance observations from Landsat-5 over the Southern Great Plains site of the ARM (Atmospheric Radiation Measurement) program gives very similar regional statistics and histograms, but significant differences at the pixel level. NDNR can be also combined with the inverse NIPA (Nonlocal Independent Pixel Approximation) of Marshak (1998) which is applied for the first time on overcast Landsat scene subscenes. We demonstrate the sensitivity of the NIPA-retrieved cloud fields on the parameters of the method and discuss practical issues related to the optimal choice of these parameters.

Orepoulos, Lazaros↗
NASA NTRS1998

Theoretical Studies of Starburst Infrared Emission: Luminosity Indicators in Dusty Photoionized Environments

The luminosity of the central source in ionizing radiation is an essential parameter in a photoionized environment and is one of the most fundamental physical quantities one can measure. We outline a method of determining the luminosity for any emission-line region using only infrared data. In dusty environments, grains compete with hydrogen in absorbing continuum radiation. Grains produce infrared emission, and hydrogen produces recombination lines. We have computed a very large variety of photoionization models, using ranges of abundances, grain mixtures, ionizing continua, densities, and ionization parameters. The conditions were appropriate for such diverse objects as H(II) regions, planetary nebulae, starburst galaxies, and the narrow- and broad-line regions of active nuclei. The ratio of the total thermal grain emission relative to H-Beta (IR/H-Beta) is the primary indicator of whether the cloud behaves as a classical Stroemgren sphere (a hydrogen-bounded nebula) or whether grains absorb most of the incident continuum (a dust-bounded nebula). We find two global limits: when IR/H-Beta < 100, infrared recombination lines determine the source luminosity in ionizing photons; when IR/H-Beta >> 100, the grains act as a bolometer to measure the luminosity.

Bottorff, Mark↗
NASA NTRS2006

A New Model for Water Vapor/Ice Abundance in a Protoplanetary Nebula

Water is a unique substance in the protoplanetary nebula since both solid and gaseous phases coexist in large quantities. Quantitative estimates of their relative abundances are important parameters regarding the physical state of the nebula and planet formation processes. This new model is based on computing the chemical evolution of water molecules until its partial pressure is sufficient to pierce the vapor pressure curve for water. The point at which this occurs relative to its steady state values determines final gas/ice ratios. The wide range of temperatures and densities in typical protoplanetary disks result in a range of gadice ratios. It is found that although ice dominates the mid and far nebula, water vapor is predominant in the centerplane region of the near nebula and above the disk photosphere. An interesting near nebula effect is the appearance of a cloud of water ice at the temperature inversion elevation surrounded by vapor above and below. This work is partially supported by the NASA Astrobiology Institute.

Davis, Sanford S.↗
NASA NTRS2012

3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer

Determining the Z-R relationship (where Z is the radar reflectivity factor and R is rainfall rate) from disdrometer data has been and is a common goal of cloud physicists and radar meteorology researchers. The usefulness of this quantity has traditionally been limited since radar represents a volume measurement, while a disdrometer corresponds to a point measurement. To solve that problem, a 3D-DSD (drop-size distribution) method of determining an equivalent 3D Z-R was developed at the University of Central Florida and tested at the Kennedy Space Center, FL. Unfortunately, that method required a minimum of three disdrometers clustered together within a microscale network (.1-km separation). Since most commercial disdrometers used by the radar meteorology/cloud physics community are high-cost instruments, three disdrometers located within a microscale area is generally not a practical strategy due to the limitations of these kinds of research budgets. A relatively simple modification to the 3D-DSD algorithm provides an estimate of the 3D-DSD and therefore, a 3D Z-R measurement using a single disdrometer. The basis of the horizontal extrapolation is mass conservation of a drop size increment, employing the mass conservation equation. For vertical extrapolation, convolution of a drop size increment using raindrop terminal velocity is used. Together, these two independent extrapolation techniques provide a complete 3DDSD estimate in a volume around and above a single disdrometer. The estimation error is lowest along a vertical plane intersecting the disdrometer position in the direction of wind advection. This work demonstrates that multiple sensors are not required for successful implementation of the 3D interpolation/extrapolation algorithm. This is a great benefit since it is seldom that multiple sensors in the required spatial arrangement are available for this type of analysis. The original software (developed at the University of Central Florida, 1998.- 2000) has also been modified to read standardized disdrometer data format (Joss-Waldvogel format). Other modifications to the software involve accounting for vertical ambient wind motion, as well as evaporation of the raindrop during its flight time.

Lane, John↗
NASA NTRS2002

RF Manipulation of Ions in the High Performance Antiproton Trap (HiPAT)

The annihilation of antimatter provides the highest mass specific energy of any other known reaction. Proper harnessing of this energy holds great promise for future space propulsion systems. Many different propulsion concepts have been proposed that take advantage of antimatter, either using matter-antimatter as the primary fuel, or as a 'spark plug' for fusion and fission systems. In order to begin to address these concepts experimentally, a method of storing and transporting antimatter must be developed. The High Performance Antiproton Trap (HiPAT) is a first-generation storage and transportation device designed to store and transport 10(exp 12) antiprotons with a storage half-life of 18 days. It uses a Penning-Malmberg ion trap with a 4T magnetic field and 20 kV potential. This will enable researchers much more flexibility in the design of antimatter experiments related to propulsion. Ions cannot be stored indefinitely in a real trap, as ion cloud instabilities develop from imperfections in manufacturing and misalignments in assembly. Previous work has been done at both the National Institute of Standards and University of California in San Diego in using RF (radio frequency) signals to both diagnose and confine the ion cloud. Two electrodes in the trap have been segmented to allow both reception and transmission of RF waves in the ion cloud. Experiments are underway to determine the number of ions and density in the cloud by "listening" to protons contained in the HiPAT. Currently we believe the density of ions stored in the trap is roughly 10(exp 15) m(exp -3). Development of non-destructive techniques is vital to the project goals, enabling continuous monitoring of the quantities stored in the system. Experimental work is also being done in identifying RF transmission frequencies that can manipulate the density of the cloud, by exchanging energy and momentum between the RF wave and the ions. Preliminary experiments have demonstrated this interaction.

Pearson, J. Boise↗
DOE OSTI2025

Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations With ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator

This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3 km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of weather states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on weather state is much larger than the seasonal variation, highlighting weather state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3 km and overestimates above 3 km for each weather state. But the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of supercooled liquid water cloud from the ceilometer observations. Ceilometer low-level supercooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low-level clouds in the reanalyses.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

An Approach to Experimental Design for the Computer Analysis of Complex Phenomenon

The ability to make credible system assessments, predictions and design decisions related to engineered systems and other complex phenomenon is key to a successful program for many large-scale investigations in government and industry. Recently, many of these large-scale analyses have turned to computational simulation to provide much of the required information. Addressing specific goals in the computer analysis of these complex phenomenon is often accomplished through the use of performance measures that are based on system response models. The response models are constructed using computer-generated responses together with physical test results where possible. They are often based on probabilistically defined inputs and generally require estimation of a set of response modeling parameters. As a consequence, the performance measures are themselves distributed quantities reflecting these variabilities and uncertainties. Uncertainty in the values of the performance measures leads to uncertainties in predicted performance and can cloud the decisions required of the analysis. A specific goal of this research has been to develop methodology that will reduce this uncertainty in an analysis environment where limited resources and system complexity together restrict the number of simulations that can be performed. An approach has been developed that is based on evaluation of the potential information provided for each "intelligently selected" candidate set of computer runs. Each candidate is evaluated by partitioning the performance measure uncertainty into two components - one component that could be explained through the additional computational simulation runs and a second that would remain uncertain. The portion explained is estimated using a probabilistic evaluation of likely results for the additional computational analyses based on what is currently known about the system. The set of runs indicating the largest potential reduction in uncertainty is then selected and the computational simulations are performed. Examples are provided to demonstrate this approach on small scale problems. These examples give encouraging results. Directions for further research are indicated.

Rutherford, Brian↗
NASA NTRS2010

Relating Convective and Stratiform Rain to Latent Heating

The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate-high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.58 horizontal resolution, the occurrence of conditional rain rates over 100 mm/day is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations. An improved convective-stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm.

Tao, Wei-Kuo↗
NASA NTRS2000

Gravity Wave Energetics Determined From Coincident Space-Based and Ground-Based Observations of Airglow Emissions

Significant progress was made toward the goals of this proposal in a number of areas during the covered period. Section 5.1 contains a copy of the originally proposed schedule. The tasks listed below have been accomplished: (1) Construction of space-based observing geometry gravity wave model. This model has been described in detail in the paper accompanying this report (Section 5.2). It can simulate the observing geometry of both ground-based, and orbital instruments allowing comparisons to be made between them. (2) Comparisons of relative emission intensity, temperatures, and Krassovsky's ratio for space- and ground-based observing geometries. These quantities are used in gravity wave literature to describe the effects of the waves on the airglow. (3) Rejection of Bates [1992], and Copeland [1994] chemistries for gravity wave modeling purposes. Excessive 02(A(sup 13)(Delta)) production led to overproduction of O2(b(sup 1)(Sigma)), the state responsible for the emission of O2. Atmospheric band. Attempts were made to correct for this behavior, but could not adequately compensate for this. (4) Rejection of MSX dataset due to lack of coincident data, and resolution necessary to characterize the waves. A careful search to identify coincident data revealed only four instances, with only one of those providing usable data. Two high latitude overpasses and were contaminated by auroral emissions. Of the remaining two mid-latitude coincidences, one overflight was obscured by cloud, leaving only one ten minute segment of usable data. Aside from the statistical difficulties involved in comparing measurements taken in this short period, the instrument lacks the necessary resolution to determine the vertical wavelength of the gravity wave. This means that the wave cannot be uniquely characterized from space with this dataset. Since no observed wave can be uniquely identified, model comparisons are not possible.

Source record↗
NASA NTRS1986

Interstellar isomers

Both observational and theoretical studies of molecular clouds are hindered by many difficulties. One way to partially circumvent the difficulties of characterizing the chemistry within these objects is to study the relative abundances of isomers which are synthesized from a common set of precursors. Unfortunately, only one such system has been confirmed, the HCN/HNC pair of isomers. While the basic outlines of its chemistry have been known for some years, there are still many aspects of the chemistry which are unclear. Another potential pair of isomers is HCO+/HOC+; HCO+ is an abundant instellar molecule and a tentative identification of HOC+ has been made in Sgr B2. This identification is being challenged, however, based on theoretical and laboratory evidence that HOC+ reacts with H2. Another potential pair of interstellar isomers is methyl cyanide (CH3CN, acetonitrile) and methyl isocyanide (CH3NC). The cyanide is well known, however the isocyanide has yet to be observed despite theoretical predictions that appreciable quantities should be present.

Defrees, D.↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: NASA Case Studies

There is an increasing interest in applying methods based on Machine Learning Techniques(MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
DOE OSTI2024

Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds

The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity observations have the potential to provide the LWC profile. Previous differential frequency radar reflectivity (DFR) efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote sensing and in situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 g m -3 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65 % relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 g m -2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1951

Meteorological Analysis of Icing Conditions Encountered in Low-Altitude Stratiform Clouds

Liquid-water content, droplet size, and temperature data measured during 22 flights in predominatly stratiform clouds through the 1948-49 and the 1949-50 winters are presented. Several icing encounters were of greater severity than those previously measured over the same geographical area, but were within the limits of similar measurements obtained over different terrain within the United States. An analysis of meteorological conditions existing during the 74 flights conducted for four winters indicated an inverse relation of liquid-water concentration to maximum horizontal extent of icing clouds. Data on the vertical extent of supercooled clouds are also presented. Icing conditions were most likely to occur in the southwest and northwest quadrants of a cyclone area, and least likely to occur in the southeast and northeast quadrants where convergent air flow and lifting over the associated warm frontal surface usually cause precipitation. Additional data indicated that, icing conditions were usually encountered in nonprecipitating clouds existing at subfreezing temperatures and were unlikely over areas where most weather observing stations reported the existence of precipitation. Measurements of liquid-water content obtained during 12 flights near the time and location of radiosonde observations were compared with theoretical values. The average liquid-water content of a cloud layer, as measured by the multicylinder technique, seldom exceeded two-thirds of that which could be released by adiabatic lifting. Local areas near the cloud tops equaled or occasionally exceeded the calculated maximum quantity of liquid water.

Kline, D. B.↗
NASA NTRSDate not supplied

A Newly Developing Community-Oriented Data System from NASA GES DISC

Data services are essential to facilitate data access and to aid efficiency of conducting research and application activities. With emerging technologies such as cloud computing and AI/ML (Artificial Intelligence/Machine Learning) leading the pace of the data world, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), home to the permanent archive for multidisciplinary Earth Observation (EO) geospatial data to study atmospheric composition, weather and climate variability, and water and energy cycles is no exception.Interfacing directly with users as part of data center work, we understand the challenges for the required time and effort to discover, visualize, and analyze large varieties and quantities of Earth Observation information for research, monitoring, and decision-making, largely due to the existing data and information systems aim to support experienced users, but has been proved difficult for non-earth scientists and new users that are unfamiliar with the variety of formats and structures in which data, metadata, and information are stored, as well as the required methods to use them. To address these challenges, I will update our latest activities with regard to water-and energy-related products and community-oriented and user-friendly services at the GES DISC, including our plans for the emerging technologies.

Jennifer Wei↗