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Leptoukh, Gregory

Publications and source records attributed to Leptoukh, Gregory.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Effects of Data Quality on the Characterization of Aerosol Properties from Multiple Sensors

Cross-comparison of aerosol properties between ground-based and spaceborne measurements is an important validation technique that helps to investigate the uncertainties of aerosol products acquired using spaceborne sensors. However, it has been shown that even minor differences in the cross-characterization procedure may significantly impact the results of such validation. Of particular consideration is the quality assurance I quality control (QA/QC) information - an auxiliary data indicating a "confidence" level (e.g., Bad, Fair, Good, Excellent, etc.) conferred by the retrieval algorithms on the produced data. Depending on the treatment of available QA/QC information, a cross-characterization procedure has the potential of filtering out invalid data points, such as uncertain or erroneous retrievals, which tend to reduce the credibility of such comparisons. However, under certain circumstances, even high QA/QC values may not fully guarantee the quality of the data. For example, retrievals in proximity of a cloud might be particularly perplexing for an aerosol retrieval algorithm, resulting in an invalid data that, nonetheless, could be assigned a high QA/QC confidence. In this presentation, we will study the effects of several QA/QC parameters on cross-characterization of aerosol properties between the data acquired by multiple spaceborne sensors. We will utilize the Multi-sensor Aerosol Products Sampling System (MAPSS) that provides a consistent platform for multi-sensor comparison, including collocation with measurements acquired by the ground-based Aerosol Robotic Network (AERONET), The multi-sensor spaceborne data analyzed include those acquired by the Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and CalipsoCALIOP satellite instruments.

Petrenko, Maksym↗
NASA NTRS2011

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Albayrak, Arif↗
NASA NTRS2011

Toward a Coherent Detailed Evaluation of Aerosol Data Products from Multiple Satellite Sensors

Atmospheric aerosols represent one of the greatest uncertainties in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood, there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource, an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, TerraMISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MA~SS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.

Ichoku, Charles↗
NASA NTRS2011

Capturing, Harmonizing and Delivering Data and Quality Provenance

Satellite remote sensing data have proven to be vital for various scientific and applications needs. However, the usability of these data depends not only on the data values but also on the ability of data users to assess and understand the quality of these data for various applications and for comparison or inter-usage of data from different sensors and models. In this paper, we describe some aspects of capturing, harmonizing and delivering this information to users in the framework of distributed web-based data tools.

Leptoukh, Gregory↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
NASA NTRS2011

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Liu, Zhong↗
NASA NTRS2011

Hurricane Data Analysis Tool

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

Liu, Zhong↗
NASA NTRS2010

Results of the Collaborative Energy and Water Cycle Information Services (CEWIS) Workshop on Heterogeneous Dataset Analysis Preparation

In support of the NASA Energy and Water Cycle Study (NEWS), the Collaborative Energy and Water Cycle Information Services (CEWIS), sponsored by NEWS Program Manager Jared Entin, was initiated to develop an evolving set of community-based data and information services that would facilitate users to locate, access, and bring together multiple distributed heterogeneous energy and water cycle datasets. The CEWIS workshop, June 15-16, 2010, at NASA/GSFC, was the initial step of the process, starting with identifying and scoping the issues, as defined by the community.

Kempler, Steven↗
NASA NTRS2010

3D Online Visualization and Synergy of NASA A-Train Data Using Google Earth

This poster presentation reviews the use of Google Earth to assist in three dimensional online visualization of NASA Earth science and geospatial data. The NASA A-Train satellite constellation is a succession of seven sun-synchronous orbit satellites: (1) OCO-2 (Orbiting Carbon Observatory) (will launch in Feb. 2013), (2) GCOM-W1 (Global Change Observation Mission), (3) Aqua, (4) CloudSat, (5) CALIPSO (Cloud-Aerosol Lidar & Infrared Pathfinder Satellite Observations), (6) Glory, (7) Aura. The A-Train makes possible synergy of information from multiple resources, so more information about earth condition is obtained from the combined observations than would be possible from the sum of the observations taken independently

Chen, Aijun↗
NASA NTRS2010

NASA Giovanni Tool for Visualization and Analysis Support for the YOTC Program

The fundamental challenges to overcoming our shortcomings in understanding and modeling/predicting tropical convection have been two folds: I) the need to represent the broad range of scales applicable to the tropical organization problem (i.e. cumulus to planetary), and II) the lack of observations that adequately and simultaneously characterize this broad range of scales and that also provide three-dimensional information on thermodynamic, radiative and dynamical interactions, including cloud microphysical processes. Because the goal of Overview of Year Of Tropical Convection (YOTC) involves examining a scientifically complex, multi-scale "process", rather than documenting the characteristics of a single parameter (e.g., SST, cloud cover), YOTC has an IOP perspective that targets a period, May 2008 - April 2010, long enough to encompass many cases of tropical convection activity in many of its most challenging yet influential forms. This includes mesoscale and synoptic variability, easterly waves and hurricanes, convectively coupled waves, the MJO, and the culmination of these in terms of the monsoon, their interactions with the extra-tropics, and mean characteristics such as tropical-to-subtropical transitions. The YOTC time period and length are driven in part by the following: 1) keeping the multi-sensor/multi-platform and model-analyses data sets and associated infrastructure manageable, 2) facilitating a focused effort by the research and operational communities on a specific scientific problem, and 3) capitalizing on the recent key additions to the armada of satellites (e.g., CloudSat and CALIPSO).

Ostrenga, Dana↗
NASA NTRS2010

Issues and Solutions for Bringing Heterogeneous Water Cycle Data Sets Together

The water cycle research community has generated many regional to global scale products using data from individual NASA missions or sensors (e.g., TRMM, AMSR-E); multiple ground- and space-based data sources (e.g., Global Precipitation Climatology Project [GPCP] products); and sophisticated data assimilation systems (e.g., Land Data Assimilation Systems [LDAS]). However, it is often difficult to access, explore, merge, analyze, and inter-compare these data in a coherent manner due to issues of data resolution, format, and structure. These difficulties were substantiated at the recent Collaborative Energy and Water Cycle Information Services (CEWIS) Workshop, where members of the NASA Energy and Water cycle Study (NEWS) community gave presentations, provided feedback, and developed scenarios which illustrated the difficulties and techniques for bringing together heterogeneous datasets. This presentation reports on the findings of the workshop, thus defining the problems and challenges of multi-dataset research. In addition, the CEWIS prototype shown at the workshop will be presented to illustrate new technologies that can mitigate data access roadblocks encountered in multi-dataset research, including: (1) Quick and easy search and access of selected NEWS data sets. (2) Multi-parameter data subsetting, manipulation, analysis, and display tools. (3) Access to input and derived water cycle data (data lineage). It is hoped that this presentation will encourage community discussion and feedback on heterogeneous data analysis scenarios, issues, and remedies.

Acker, James↗
NASA NTRS2010

Cross-Characterization of Aerosol Properties from Multiple Spaceborne Sensors Facilitated by Regional Ground-Based Observations

Aerosol observations from space have become a standard source for retrieval of aerosol properties on both regional and global scales. Indeed, the large number of currently operational spaceborne sensors provides for unprecedented access to the most complete set of complimentary aerosol measurements ever to be available. Nonetheless, this resource remains under-utilized, largely due to the discrepancies and differences existing between the sensors and their aerosol products. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have designed and implemented an online Multi-sensor Aerosol Products Sampling System (MAPSS) that facilitates the joint sampling of aerosol data from multiple sensors. MAPSS consistently samples aerosol products from multiple spaceborne sensors using a unified spatial and temporal resolution, where each dataset is sampled over Aerosol Robotic Network (AERONET) locations together with coincident AERONET data samples. In this way, MAPSS enables a direct cross-characterization and data integration between aerosol products from multiple sensors. Moreover, the well-characterized co-located ground-based AERONET data provides the basis for the integrated validation of these products.

Petrenko, Maksym↗