Gregory G. Leptoukh

Online analysis enhances use of NASA Earth science data

Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5–10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.

Giovanni: A Web Service Workflow-Based Data Visualization and Analysis System

NASAs Goddard Earth Sciences Data and Information Services Center has developed the Goddard Interactive Online Visualization ANd aNalysis Infrastructure or ldquoGiovanni,rdquo an asynchronous Web-service-based workflow management system for Earth science data. Giovanni has been providing an intuitive and responsive interface for visualizing, analyzing, and intercomparing multisensor data using only a Web browser to scientists and other users. Giovanni supports many types of single- and multiparameter visualizations and statistical analyses. The interface also provides users with capabilities for downloading images and data in multiple formats. Giovanni supports open and standard data protocols and formats. Finally, Giovanni provides users with a data lineage that describes, in detail, the algorithms used in processing the data including caveats and other scientifically pertinent information.

Development of a Web-based visualization platform for climate research using Google Earth

Recently, it has become easier to access climate data from satellites, ground measurements, and models from various data centers. However, searching, accessing, and processing heterogeneous data from different sources are very time-consuming tasks. There is lack of a comprehensive visual platform to acquire distributed and heterogeneous scientific data and to render processed images from a single accessing point for climate studies. This paper documents the design and implementation of a Web-based visual, interoperable, and scalable platform that is able to access climatological fields from models, satellites, and ground stations from a number of data sources using Google Earth (GE) as a common graphical interface. The development is based on the TCP/IP protocol and various data sharing open sources, such as OPeNDAP, GDS, Web Processing Service (WPS), and Web Mapping Service (WMS). The visualization capability of integrating various measurements into GE extends dramatically the awareness and visibility of scientific results. Using embedded geographic information in the GE, the designed system improves our understanding of the relationships of different elements in a four-dimensional domain. The system enables easy and convenient synergistic research on a virtual platform for professionals and the general public, greatly advancing global data sharing and scientific research collaboration.

Seasonal Variations of Chlorophyll

Monthly climatology of chlorophyll concentration (chl ) based on nine years of SeaWiFS data is used to illustrate seasonal variations and spatial structures in the northern South China Sea (SCS). Chl starts to increase in September at the northern coast of Luzon Island, continues to increase in the autumn, and reaches its maximum in December or January. Maximum chl is centered in the northern SCS off the northwestern coast of Luzon Island. Chl starts to decrease gradually in February, and its values become very low from June to August. The region of elevated chl during the winter bloom season is funnel shaped, with the narrow end at the northern coast of Luzon Island, where the chl value is highest and opening toward the northwest. The sea surface temperature (SST) in this funnel-shaped region is significantly colder than SST in surrounding regions of the same latitude. The present study indicates that the winter blooms indicated by higher chl and colder SST in the northern SCS are linked strongly to the local winter monsoon. The initial data exploration and analysis presented in this study was carried out using Giovanni, a state-of-the-art Web-based data analysis and visualization tool.

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The Sea-viewing Wide Field-of View Sensor (SeaWiFS) Mission has initiated a new era of ocean color remote sensing and has established performance benchmarks that will be emulated by subsequent missions. An integral element of the SeaWiFS mission is the data component, performed by the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), NASA Goddard Space Flight Center, Greenbelt, MD. Since the beginning of data distribution in September 1997, the GES DAAC has managed the data archive and improved data distribution capability. SeaWiFS data products are archived in a primary, secondary, and tertiary archive structure, ensuring data preservation. Data distribution utilizes a World Wide Web (WWW)-based ordering interface, allowing distribution either electronically or on magnetic tape media. Automatic data subscriptions, supplying user-tailored data product selections, have yielded a high archive-to-distribution ratio. System improvements have increased efficiency and redundancy. The user interface has added features designed to facilitate data access and data usage, enhanced by WWW information resources and comprehensive online dataset documentation. As SeaWiFS enters the latter half of its five-year mission, a system performance assessment provides useful information for other Earth remote sensing missions and allows consideration of future usage objectives for the SeaWiFS data archive.

Concentration in the Northern South China Sea

Coastal regions around the globe represent a major source for anthropogenic aerosols in the atmosphere, but the surface characteristics may not be optimal for the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms designed for aerosol retrievals over dark land or ocean surfaces. Using data collected from 62 coastal stations worldwide by the Aerosol Robotic Network (AERONET) in 2002–2011, statistical assessments of uncertainties are conducted for coastal aerosol optical depth (AOD) retrieved from MODIS measurements aboard the Aqua satellite (i.e., the Collection 5.1 MYD04 data product generated by the MODIS atmosphere group). It is found that coastal AODs (at 550 nm) characterised respectively by the Dark Land algorithm and the Dark Ocean algorithm all exhibit a log-normal distribution, which contrasts to the near-normal distribution of their corresponding biases. After data filtering using quality flags, the MODIS AODs from both the Dark Land and Dark Ocean algorithms over coastal regions are highly correlated with AERONET AODs (R 2≈0.8), but both have larger uncertainties than their counterparts (of MODIS AODs) over land and open ocean. Overall, the Dark Ocean algorithm overestimates the AERONET coastal AOD by 0.021 for AOD < 0.25 and underestimates it by 0.029 for AOD > 0.25. This dichotomy is shown to be related to the ocean-surface wind speed and cloud-contamination effects on the MODIS aerosol retrievals. Consequently, an empirical correction scheme is formulated that uses cloud fraction and sea-surface wind speed from Modern Era Retrospective-Analysis for Research and Applications (MERRA) to correct the AOD bias from the Dark Ocean algorithm, and it is shown to be effective over the majority of the coastal AERONET stations to (a) simultaneously reduce both the mean and the spread of the bias and (b) improve the trend analysis of AOD. Further correlation analysis performed after such an empirical bias correction shows that the MODIS AOD is also likely impacted by the concentration of suspended particulate matter in coastal waters, which is not taken into account during the MODIS AOD retrievals. While mathematically the MODIS AODs over the global coastal AERONET sites show statistically significant discrepancies (p<1%) from their respective AERONET-measured counterparts in terms of mean and frequency, different applications of MODIS AODs in climate and air-quality studies often have their own tolerances of uncertainties. Nevertheless, it is recommended that an improved treatment of varying sea-surface wind and sediment over coastal waters be an integral part in the continuous evolution of the MODIS AOD retrieval algorithms.

SeaWiFS ocean color data archive and distribution system: assessment of system performance

This paper describes the satellite data processing and services that constitute current functionalities of the A-Train Data Depot. We first provide a brief introduction to the original geometrical intricacies of the platforms and instruments of the A-Train constellation and then proceed with a description of our A-Train collocation-processing algorithm that provides subsets that facilitate synergistic use of the various instruments. Finally, we present some sample image products from our web-based Giovanni tool which allows users to display, compare, and download coregistered A-Train-related data.

Long-term statistical assessment of Aqua-MODIS aerosol optical depth over coastal regions: bias characteristics and uncertainty sources

This paper describes the air quality data products and services available through Giovanni, a web based tool for access, visualization, and analysis of satellite remote sensing products, and also model output and surface observations relevant to global air quality. Available datasets include total column aerosol measurements from numerous satellite instruments, column NO2 and SO2, vertical aerosol products from CALIPSO, surface PM2.5 concentrations over the continental U.S, and speciated model Aerosol Optical Depth. Giovanni was designed to make satellite and ground-based data easier to use; it does not require separate access to or downloading of data sets, making the visualizations and analysis services accessible to both the novice and the experienced user. Giovanni air quality data products are provided on a common grid and can also be obtained in KMZ format for Google Earth visualization. This feature allows collocation of datasets to aid in analysis of pollution events and to facilitate satellite/monitor comparisons and aerosol intercomparison studies in a fraction of the time compared to traditional methods. Giovanni also supports multiple interoperability protocols which permit data sharing with other online tools, in order to enhance access to the datasets for improved air quality decision making. The Giovanni team is currently actively involved in several data networking initiatives with service oriented tools at other institutions such as DataFed.

A-Train data depot - bringing Atmospheric measurements together

Abstract Over the decades, significant progress has been made in satellite precipitation product development. In particular, temporal resolution and timely availability have been improved by blended techniques. The resulting products, including near-real-time precipitation products, are widely used in various research and applications. However, the lack of support for user-defined areas or points of interest poses a major obstacle to quickly gaining knowledge of product quality and behavior on a local or regional scale. Current online operational intercomparison and validation services have not addressed this issue adequately. This paper describes an ongoing work to develop an online information system prototype for global satellite precipitation algorithm validation and intercomparison, to overcome current shortcomings by providing dynamic and customized information to users on the expected bias and accuracy of the products, and to give algorithm developers a better understanding of the strengths and wea...

Access, Visualization, and Interoperability of Air Quality Remote Sensing Data Sets via the Giovanni Online Tool

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging-interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging-interpolation procedure can produce complete spatial fields with acceptable errors.