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NASA NTRS2005

Statistical Considerations of Data Processing in Giovanni Online Tool

The GES DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni) is a web-based interface for the rapid visualization and analysis of gridded data from a number of remote sensing instruments. The GES DISC currently employs several Giovanni instances to analyze various products, such as Ocean-Giovanni for ocean products from SeaWiFS and MODIS-Aqua; TOMS & OM1 Giovanni for atmospheric chemical trace gases from TOMS and OMI, and MOVAS for aerosols from MODIS, etc. (http://giovanni.gsfc.nasa.gov) Foremost among the Giovanni statistical functions is data averaging. Two aspects of this function are addressed here. The first deals with the accuracy of averaging gridded mapped products vs. averaging from the ungridded Level 2 data. Some mapped products contain mean values only; others contain additional statistics, such as number of pixels (NP) for each grid, standard deviation, etc. Since NP varies spatially and temporally, averaging with or without weighting by NP will be different. In this paper, we address differences of various weighting algorithms for some datasets utilized in Giovanni. The second aspect is related to different averaging methods affecting data quality and interpretation for data with non-normal distribution. The present study demonstrates results of different spatial averaging methods using gridded SeaWiFS Level 3 mapped monthly chlorophyll a data. Spatial averages were calculated using three different methods: arithmetic mean (AVG), geometric mean (GEO), and maximum likelihood estimator (MLE). Biogeochemical data, such as chlorophyll a, are usually considered to have a log-normal distribution. The study determined that differences between methods tend to increase with increasing size of a selected coastal area, with no significant differences in most open oceans. The GEO method consistently produces values lower than AVG and MLE. The AVG method produces values larger than MLE in some cases, but smaller in other cases. Further studies indicated that significant differences between AVG and MLE methods occurred in coastal areas where data have large spatial variations and a log-bimodal distribution instead of log-normal distribution.

Suhung, Shen↗
NASA NTRS2004

Distributive On-line Processing, Visualization and Analysis System for Gridded Remote Sensing Data

The ability to use data stored in the current Earth Observing System (EOS) archives for studying regional or global phenomena is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. Gaining this understanding and applying it to data reduction is a time- consuming task that must be undertaken before the core investigation can begin. This is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets that are usually of different formats, structures, and resolutions, for example, when preparing data for input into modeling systems. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step towards meeting this challenge by developing an infrastructure with a Web interface that allows users to perform interactive analysis online without downloading any data, the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni." Giovanni provides interactive, online, analysis tools for data users to facilitate their research. There have been several instances of this interface created to serve TRMM users, Aerosol scientists, Ocean Color and Agriculture applications users. The first generation of these tools support gridded data only. The user selects geophysical parameters, area of interest, time period; and the system generates an output on screen in a matter of seconds. The currently available output options are: Area plot averaged or accumulated over any available data period for any rectangular area; Time plot time series averaged over any rectangular area; Time plots image view of any longitude-time and latitude-time cross sections; ASCII output for all plot types; Image animation for area plot. In the future, we will add correlation plots, GIS-compatible outputs, etc. This allow user to focus on data content (i.e. science parameters) and eliminate the need for expensive learning, development and processing tasks that are redundantly incurred by an archive's user community. The current implementation utilizes the GrADS-DODS Server (GDS), a stable, secure data server that provides subsetting and analysis services across the Internet for any GrADS-readable dataset. The subsetting capability allows users to retrieve a specified temporal and/or spatial subdomain from a large dataset, eliminating the need to download everything simply to access a small relevant portion of a dataset. The analysis capability allows users to retrieve the results of an operation applied to one or more datasets on the server. In our case, we use this approach to read pre-processed binary files and/or to read and extract the needed parts from HDF or HDF-EOS files. These subsets then serve as inputs into GrADS processing and analysis scripts. It can be used in a wide variety of Earth science applications: climate and weather events study and monitoring; modeling. It can be easily configured for new applications.

Leptoukh, G.↗
NASA NTRS2017

AIRS-Only Product in Giovanni for Exploring Up-to-Date AIRS Observation and Comparing with AIRS+AMSU Product

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been the home of processing, archiving, and distribution services for the Atmospheric Infrared Sounder (AIRS) mission since its launch in 2002 for global observations of the atmospheric state. Giovanni, a Web-based application developed by the GES DISC, provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data without having to download the data. Most important AIRS variables, including temperature and humidity profiles, outgoing longwave radiation, cloud properties, and trace gases, are available in Giovanni. AIRS is an instrument suite comprised of a hyperspectral infrared instrument (AIRS) and two multichannel microwave instruments, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB). As HSB ceased operation in the very early stages of the AIRS mission, the AIRS project operates two parallel retrieval algorithms: one using both IR and MW measurements (AIRS+AMSU) and the other using only IR measurements (AIRS-only), which covers most of the mission duration. The AIRS+AMSU product is better quality, and the variables in Giovanni are from this product. However, generation of the AIRS+AMSU product has been suspended since the AMSU instrument anomaly occurred in late September 2016. To continue exploring up-to-date AIRS observations, the same set of variables from the AIRS-only product have been added to Giovanni by the GES DSIC. This will also support comparison of AIRS-only with AIRS+AMSU retrievals. In this presentation, we demonstrate the visualization of the AIRS-only product and plots/statistics of comparison with AIRS+AMSU product using Giovanni.

AIRS↗
NASA NTRS2007

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Leptoukh, Gregory↗
NASA NTRSDate not supplied

Using OPeNDAP In The Cloud to Connect NASA Data Centers and Support Open Data Access

NASA DAACs (Distributed Active Archive Centers), including the Goddard Earth Sciences Data Information and Services Center (GES DISC), are currently transitioning from on-premises servers to a shared Earthdata Cloud in order to build more interoperability, cross-collaboration, and streamlined services between their data centers. Migrating its on-premises OPeNDAP service to the cloud is a critical component of making this interconnectedness between NASA DAACs possible. To improve their cloud services, GES DISC is leveraging open-source platforms like Github to collect user feedback, create use cases, and develop resources to enable real-time learning for users about OPeNDAP in the cloud. This presentation gives an overview of the OPeNDAP in the cloud, resources developed to access this service, and considerations for improved user guidance and experience to further support NASA's commitment to the Open-Source Science Initiative (OSSI).

Christopher Battisto↗
NASA NTRSDate not supplied

Latest Development in Radiative Transfer Models and Retrieval Algorithms Using Principal Components

The radiative transfer model (RTMs) has a wide range of applications in satellite remote sensing and atmospheric radiation applications. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast and accurate RTMs are needed to efficiently process large amount of satellite data. A Principal Component-based Radiative Transfer Model (PCRTM) was first developed in 2004 at NASA Langley Research Centre to fulfil this need. By using PC-compression, one can reduce the data dimension significantly while maintaining original information content. The PCRTM can directly compute PC-scores and their derivatives with respect to retrieved parameters. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 0.250 µm (400000 cm-1) to 2000 µm (50 cm-1) with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks (0.03 K RMS error in IR and 0.05% in solar). The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, FIRST, and CLARREO-IR in thermal IR spectral region and CLARREO-Solar, CPF, TEMPO, EMIT, OMI, and SCIAMACHY in solar spectral region. The PCRTM accuracy has been demonstrated via RTM intercomparisons and with real satellite observations from AIRS, CrIS, IASI, SCHIAMACHY, and EMIT etc. In this presentation, we will describe two PCRTM-based inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from hyperspectral sounders such as AIRS, CrIS, IASI, and NAST-I. The first one is called Single Fieldof-view Sounder Atmospheric Product (SiFSAP) algorithm. It provides L2 products with 9-times higher area spatial resolution as compared to current cloud-clearing sounder algorithms. The SiFSAP L2 and L3 products are available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The second inversion algorithm is called Climate Fingerprinting Atmospheric Product (ClimFiSP) algorithm. It is designed to produce high quality climate products (trends, anomalies, daily, and monthly profiles of temperature, water vapor, traces, clouds, and surface properties) from multiple satellite sensors such AIRS on Aqua and CrIS on multiple satellites. This product will be available at NASA GES DISC later this year. The PCRTMbased high fidelity simulators for CLARREO and CPF have been used for sensor performance trade studies, algorithm development, and inter-satellite calibrations. We have also used PCRTM generated TOA radiance spectra to train an AI-based algorithm and successfully retrieved cloud properties from EMIT solar hyperspectral imagers.

PCRTM↗
NASA NTRSDate not supplied

Supervised Machine Learning Approach for Classifying Earth Science Publications

The data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding these collections' algorithms, their validation, and their applications. As NASA data centers collect these publications for public use, it is helpful to categorize them based on how they relate to their associated datasets. Specifically, whether the publication linked to the GES DISC dataset is using it for applicational research, describing the algorithm used for the dataset creation, validating the dataset, or providing a general overview of the data collection. Currently, this process requires simple manual labeling, and as such, it may be possible to solve via automation. To approach this problem, machine learning classifiers were developed to predict a publication's category. Manually labeled publications were used as the training data for the supervised machine learning algorithms, specifically Random Forest and Multinomial Naïve Bayes. After balancing the dataset and implementing the Multinomial Naïve Bayes algorithm, the classification accuracy achieved was substantially higher than the baseline accuracy, thus significantly improving the efficiency of publication labeling.

Rohan Dayal↗
NASA NTRS2004

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Teng, William↗
NASA NTRS2012

Analysis of Water and Energy Budgets and Trends Using the NLDAS Monthly Data Sets

The North American Land Data Assimilation System (NLDAS) is a collaborative project between NASA GSFC, NOAA, Princeton University, and the University of Washington. NLDAS has created surface meteorological forcing data sets using the best-available observations and reanalyses. The forcing data sets are used to drive four separate land-surface models (LSMs), Mosaic, Noah, VIC, and SAC, to produce data sets of soil moisture, snow, runoff, and surface fluxes. NLDAS hourly data, accessible from the NASA GES DISC Hydrology Data Holdings Portal, http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings, are widely used by various user communities in modeling, research, and applications, such as drought and flood monitoring, watershed and water quality management, and case studies of extreme events. More information is available at http://ldas.gsfc.nasa.gov/. To further facilitate analysis of water and energy budgets and trends, NLDAS monthly data sets have been recently released by NASA GES DISC.

Vollmer, Bruce E.↗
NASA NTRS2015

Total and Spectral Solar Irradiance Sensor (TSIS) Project Overview

The main objective of the Total and Spectral solar Irradiance Sensor (TSIS) is to acquire measurements to determine the direct and indirect effects of solar radiation on climate. TSIS total solar irradiance measurements will extend a 37-year long uninterrupted measurement record of incoming solar radiation, the dominant energy source driving the Earths climate and the most precise indicator of changes in the Suns energy output. TSIS solar spectral irradiance measurements will determine the regions of the Earths multi-layered atmosphere that are affected by solar variability, from which the solar forcing mechanisms causing changes in climate can be quantified. TSIS includes two instruments: the Total Irradiance Monitor (TIM) and the Spectral Irradiance Monitor (SIM), integrated into a single payload. The TSIS TIM and SIM instruments are upgraded versions of the two instruments that are flying on the Solar Radiation and Climate Experiment (SORCE) mission launched in January 2003. TSIS was originally planned for the nadir-pointing National Polar-orbiting Operational Environmental Satellite System (NPOESS) spacecraft. The TSIS instrument passed a Critical Design Review (CDR) for NPOESS in December 2009. In 2010, TSIS was re-planned for the Joint Polar Satellite System (JPSS) Polar Free Flyer (PFF). The TSIS TIM, SIM, and associated electronics were built, tested, and successfully completed pre-ship review as of December 2013.In early 2014, NOAA and NASA agreed to fly TSIS on the International Space Station (ISS). In the FY16 Presidents Budget, NASA assumes responsibility for the TSIS mission on ISS. The TSIS project includes requirements, interface, design, build and test of the TSIS payload, including an updated pointing system, for accommodation on the ISS. It takes advantage of the prior development of the TSIS sensors and electronics. The International Space Station (ISS) program contributions include launch services and robotic installation of the TSIS payload onto an ISS Express Logistics Carrier, mission operations, and communications. Total and Spectral solar irradiance data products will be produced, calibrated, and made publically available through the Goddard Earth Science Data and Information Services Center (GES DISC).The NASA GSFC TSIS project at GSFC is responsible for project management, system engineering, safety and mission assurance, and engineering oversight for the TSIS payload. The TSIS project has contracted with the University of Colorado Laboratory for Atmospheric and Space Physics (LASP) for the design, development and testing of TSIS, support for ISS integration, science operations of the TSIS instrument, data processing, data evaluation and delivery to the GES DISC. TSIS will be delivered to Kennedy Space Center for integration in 2017, with launch and installation onto ISS planned for late 2017-early 2018. After a 90-day check-out period, NASA plans five years of TSIS operations.

spectral emission↗
NASA NTRSDate not supplied

Cloud-Based Time Series Analysis of Extremes: Use Cases and Applications

"Extreme weather events, such as hurricanes, tornadoes, floods, droughts, heatwaves, and blizzards, can cause widespread damage, disrupting ecosystems, agricultural production, and economies. The frequency and intensity of these events have been increasing, likely due to climate change, raising concerns and the need for more accurate analysis and predictions. NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) has migrated its long-term historical datasets, including precipitation data from MERRA-2 reanalysis, GLDAS land data assimilation, and IMERG satellite observations, to the cloud. This cloud-stored data enables scientists and researchers to utilize cloud computing for advanced modeling and forecasting of extreme weather events, eliminating the need to download large datasets. In this presentation, we will provide an overview of the cloud-based data and services managed by GES DISC; demonstrate methods for accessing and analyzing time series data stored in the cloud; and compare results across various datasets to address critical questions related to extreme precipitation. We will present use cases including: 1. Determining the average total precipitation in California during January and February from 2000 to 2024, and identifying anomalous precipitation in 2021. 2. Calculating the 10, 20, 50, and 100-year return periods for maximum daily rainfall based on 25 years of historical precipitation data (2000-2024) for Maryland.

time series↗
NASA NTRS2018

Temperature Anomalies from the AIRS Product in Giovanni for the Climate Community

The Atmospheric Infrared Sounder (AIRS) mission began with the launch of Aqua in 2002. Over 15 years of AIRS products have been used by the climate research and application communities. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the present Aqua AIRS mission and the succeeding Suomi National Polar-Orbiting Partnership (SNPP) Cross-track Infrared Sounder (CrIS) mission. We generated a Multi-year Monthly Mean and Anomaly product using 14 years of AIRS standard monthly product. The product includes Air Temperature at the Surface and Surface Skin Temperature, both in Ascending/Daytime and Descending/Nighttime mode. The temperature variables and their anomalies are deployed to Giovanni, a Web-based application developed by the GES DISC. Giovanni provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data without having to download the data. It is also a powerful tool that stakeholders can use for decision support in planning and preparing for increased climate variability. In this presentation, we demonstrate the functions in Giovanni with use cases employing AIRS Multi-year Monthly Mean and Anomaly variables.

Giovann↗
NASA NTRS2017

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni↗
NASA NTRSDate not supplied

Deriving a Suite of Climate Data Records from 21-years of Sounder Observations

Two retrieval algorithms, a Single Field-of-view Sounder Atmospheric Product (SiFSAP) Level 2 algorithm and a Climate Fingerprinting Sounder Product (ClimFiSP) Level 3 algorithm, have been developed at NASA Langley Research Center. In this presentation, we will demonstrate the radiance closure build into these two algorithms. This is possible due to the use of a principal-component-based radiative transfer model (PCRTM). The PCRTM enables the inclusion of all available spectral information from hyperspectral IR sounders. It also has fast and accurate capability of modeling observed cloudy radiance spectra directly. The SiFSAP, which fits sounder radiance spectra using PCRTM as forward model and an optimal estimation as inverse model, has 3-times higher spatial resolution and 9-times denser data products comparing to current NASA and NOAA operational IR sounder products. With great support from NASA sounder SIPS, the SiFSAP algorithm has been delivered to NASA GES DISC for L2 and L3 data generation and releasing. The ClimFiSP uses a spectral fingerprinting method to directly generate L3 data products from spatiotemporally averaged L1 data. The goal of ClimFiSP is to generate high-quality climate data records (CDRs) from multiple IR sounders (e.g. Aqua AIRS, SNPP CrIS, NOAA-20 and NOAA-21 CrIS) using a consistent retrieval methodology. Using CHIRP L1 data, we have generated 20-years of CDRs for atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties. The algorithm will be transitioned to Sounder SIPS soon and eventually to NASA GES DISC.

Xu Liu↗
NASA NTRS2008

Online Visualization and Analysis of Merged Global Geostationary Satellite Infrared Dataset

The NASA Goddard Earth Sciences Data Information Services Center (GES DISC) is home of Tropical Rainfall Measuring Mission (TRMM) data archive. The global merged IR product also known as the NCEP/CPC 4-km Global (60 degrees N - 60 degrees S) IR Dataset, is one of TRMM ancillary datasets. They are globally merged (60 degrees N - 60 degrees S) pixel-resolution (4 km) IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 and GMS). The availability of data from METEOSAT-5, which is located at 63E at the present time, yields a unique opportunity for total global (60 degrees N- 60 degrees S) coverage. The GES DISC has collected over 8 years of the data beginning from February of 2000. This high temporal resolution dataset can not only provide additional background information to TRMM and other satellite missions, but also allow observing a wide range of meteorological phenomena from space, such as, mesoscale convection systems, tropical cyclones, hurricanes, etc. The dataset can also be used to verify model simulations. Despite that the data can be downloaded via ftp, however, its large volume poses a challenge for many users. A single file occupies about 70 MB disk space and there is a total of approximately 73,000 files (approximately 4.5 TB) for the past 8 years. In order to facilitate data access, we have developed a web prototype to allow users to conduct online visualization and analysis of this dataset. With a web browser and few mouse clicks, users can have a full access to over 8 year and over 4.5 TB data and generate black and white IR imagery and animation without downloading any software and data. In short, you can make your own images! Basic functions include selection of area of interest, single imagery or animation, a time skip capability for different temporal resolution and image size. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. The prototype will be integrated into GIOVANNI and existing GIOVANNI capabilities, such as, data download, Google Earth KMZ, etc will be available. Users will also be able to access other data products in the GIOVANNI family.

Liu, Zhong↗
NASA NTRS2018

GRACE-Assimilated Drought Indicators for the U.S. Drought Monitor

The Gravity Recovery and Climate Experiment (GRACE) mission detects changes in Earth's gravity field by precisely monitoring the changes in distance between two satellites orbiting the Earth in tandem. Scientists at NASA's Goddard Space Flight Center generate GRACE-assimilated groundwater and soil moisture drought indicators each week, for drought monitor-related studies and applications. The GRACE-assimilated Drought Indicator Version 2.0 data product (GRACE-DA-DM V2.0) is archived at, and distributed by, the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center). More information about the data and data access is available on the data product landing page at https://disc.gsfc.nasa.gov/datasets /GRACEDADM_CLSM0125US_7D_2.0/summary. The GRACE-DA-DM V2.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. The drought indicators are of wet or dry conditions, expressed as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2012. These GRACE-assimilated drought indicators, with improved spatial and temporal resolutions, should provide a more comprehensive and objective identification of drought conditions. This presentation describes the basic characteristics of the data and data services at NASA GES DISC and collaborative organizations, and uses a few examples to demonstrate the simple ways to explore the GRACE-assimilated drought indicator data.

hydrology↗
NASA NTRS2018

Studying Diurnal Variations of Aerosols with NASA MERRA-2 Reanalysis Data

Aerosols play an important role in atmospheric dynamics, climate variations, and Earth's energy cycle by altering the radiation balance in the atmosphere through interaction with clouds, providing fertilizer for forests and canopy, and as a supply of iron to the ocean over long time periods. Studies suggest that much of the feedback between dust aerosols and dynamics is associated with diurnal and synoptic scale variability. However, the lack of sub-daily resolution of aerosols from satellite observations makes it difficult to study the diurnal characteristics, especially over tropical and subtropical regions. Investigation of this topic utilizes over 37 years of simulated global aerosol products from NASA atmospheric reanalysis, in the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data set, available from NASA Goddard Earth Science Data and Information Services Center (GES DISC). MERRA-2 covers the period 1980-present, and is continuing as an ongoing climate analysis. Aerosol assimilation is included throughout the period, using data from MODIS, MISR, AERONET, and AVHRR (in the pre-EOS period). The aerosols are assimilated using the MERRA-2 aerosol model, which interacts directly with radiation parameterization, and is radiatively coupled with atmospheric model dynamics in the Goddard Earth Observing System Model, Version 5 (GEOS-5). Hourly, monthly, and monthly diurnal data are available at spatial resolution of 0.5o x 0.625o (latitude x longitude). By using MERRA-2 hourly and monthly diurnal products, different aerosol diurnal variabilities are observed over North America, Africa, Asia, and Australia, that may be due to different meteorological conditions and aerosol sources. The presentation will also provide an overview of MERRA-2 data services at GES DISC, such as how to find and download data, and how to quickly visualize and analyze data online with Giovanni.

aerosol↗
NASA NTRS2012

Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications

Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.-Japan satellite mission to provide the first detailed and comprehensive data set of the four-dimensional distribution of rainfall and latent heating over vastly under-sampled tropical and subtropical oceans and continents (40 S - 40 N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological and other research and application activities around the world. The purpose of this short article is to inform that the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and near-real-time precipitation data sets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor s resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Open-source Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission which is scheduled for launch in 2014.

Liu, Zhong↗