Steven Kempler

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

Precipitation is a critical component of the Earths hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.–Japan satellite mission to provide the first detailed and comprehensive dataset of the four-dimensional distribution of rainfall and latent heating over vastly undersampled 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. This short article describes how the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and nearreal- time precipitation datasets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensors 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...

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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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.

Concentration in the Northern South China Sea

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...

A-Train data depot - bringing Atmospheric measurements together

Increasingly sophisticated National Aeronautics and Space Administration (NASA) Earth science missions have driven their associated data and data management systems from providing simple point-to-point archiving and retrieval to performing user-responsive distributed multisensor information extraction. To fully maximize the use of remote-sensor-generated Earth science data, NASA recognized the need for data systems that provide data access and manipulation capabilities responsive to research brought forth by advancing scientific analysis and the need to maximize the use and usability of the data. The decision by NASA to purposely evolve the Earth Observing System Data and Information System (EOSDIS) at the Goddard Space Flight Center (GSFC) Earth Sciences (GES) Data and Information Services Center (DISC) and other information management facilities was timely and appropriate. The GES DISC evolution was focused on replacing the EOSDIS Core System (ECS) by reusing the in-house developed disk-based Simple, Scalable, Script-based Science Product Archive (S4PA) data management system and migrating data to the disk archives. Transition was completed in December 2007.

Developing an Online Information System Prototype for Global Satellite Precipitation Algorithm Validation and Intercomparison

New online prototypes have been developed to extend and enhance the previous effort by facilitating investigation of product characteristics and intercomparison of precipitation products in different algorithms as well as in different versions at different spatial scales ranging from local to global without downloading data and software. Several popular Tropical Rainfall Measuring Mission (TRMM) products and the TRMM Composite Climatology are included. In addition, users can download customized data in several popular formats for further analysis. Examples show product quality problems and differences in several monthly precipitation products. It is seen that differences in daily and monthly precipitation products are distributed unevenly in space and it is necessary to have tools such as those presented here for customized and detailed investigations. A simple time series and two area maps allow the discovery of abnormal values of 3A25 in one of the months. An example shows a V-shaped valley issue in the Version 6 3B43 time series and another example shows a sudden drop in 3A25 monthly rain rate, all of which provide important information when the products are used for long-term trend studies. Future plans include adding more products and statistical functionality in the prototypes. We developed online tools for intercomparing global precipitation products.We included 3-hourly, daily, and monthly precipitation products.We included research and near-real-time products in both Version 6 and Version 7.Basic functions help discover data quality issues and differences in products.Several data formats are available for data download and for further analysis.

Evolution of Information Management at the GSFC Earth Sciences (GES) Data and Information Services Center (DISC): 2006–2007

Google Earth, as one of most popular geospatial data visualization environment, has been used to augment the research value of Earth science data at NASA Goddard Earth Science Data and Information Service Center. The solutions of how to use Google Earth to facilitate the sharing and interaction of geospatial data are described and summarized in this paper first. Some of solutions are applied to two-dimensional mapped data to render the data into Google Earth via Earth science-specific software and keyhole markup language. A 3D model based innovative method is proposed here to visualize and display the three-dimensional atmospheric vertical profiles derived from A-Train constellation satellites in the form of 3D orbit curtain in Google Earth. This visualization capability extends awareness and visibility of NASA Earth science data to massive Google Earth user groups, including the general public. The availability of many scientific results in Google Earth enables easy and convenient synergistic research, advancing collaborative and globalized scientific research on a virtual platform.

Developing GIOVANNI-based online prototypes to intercompare TRMM-related global gridded-precipitation products

Abstract: The NASA Giovanni data analysis system ha s been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, a nd air quality research. The us e of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated ( p < 0.05) with influenza activity in both temperate and tr opical climate. In the two temperate locations

Visualization of NASA Earth science data in Google Earth

Keyhole Markup Language (KML), the de facto standard for representing, visualizing and transmitting geospatial data on Virtual Globes, lately approved by the Open Geospatial Consortium (OGC), Inc., has been widely used by the Earth Science communities. Most of the popular virtual globe systems, such as Google Earth and Microsoft Virtual Earth support KML format. This new online approach is changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. The so-called A-Train, a series of seven U.S. and international Sun-synchronous satellites, flying in tight formation just seconds to minutes apart, across the local afternoon equator, has been producing abundant measurements of vertical profiles of atmospheric parameters. This paper first discusses the key technical points for access to and visualization of three-dimensional Earth science data by using KML and Virtual Globes. Then, the Virtual Globes are taken as a virtual three-dimensional platform to synergize horizontal data and vertical profiles along the A-Train tracks to explore the scientific relationships among multiple physical phenomena. Two kinds of scientific scenarios are investigated: a) The relationships among cloud, aerosol and atmospheric temperature, and b) the relationships among cloud, wind and precipitation. The seamless visualization and synergy of multiple versatile datasets facilitate scientists to easily explore and find critical relationships between some phenomena that would not be easily found otherwise.

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

Google Earth, as the pioneer of Virtual Globes, is changing the way in which professionals acquire, assess, organize, manage, share, visualize, and utilize three-dimensional geospatial data in a virtual environment for scientific research. Google Earth is also changing how the public interacts with virtual globes to facilitate their daily life. Keyhole Markup Language (KML) is the key technology making this change possible. NASA campaign missions have collected large volumes of vertical profiles of the atmosphere for various experiments and validation of instruments to be loaded on satellites. This paper describes design and implementation of two solutions to visualizing those vertical profiles in Google Earth for facilitating the validation and testing of the instruments. The first is to read and process the scientific data into images by using Interface Description Language (IDL). Then, COLLAborative Design Activity (COLLADA) model is used to process and render these images in the form of three dimension. Finally, KML files are produced—the vertical profiles are visualized in the form of vertical curtains in Google Earth. The second way is to read the data values directly from the vertical profiles and use them to produce KML files that visualize the vertical profiles as vertical curtains in Google Earth. The vertical curtain is composed of a large number of small rectangles. The first way produces a high-resolution curtain quickly, but the positions of the vertical curtain in Google Earth are not accurate. For the second way, the positions of the vertical curtain can be very accurate in Google Earth, but the resolution is low and the speed is slow. Examples are given for both solutions.