Development of a Web Service and Android 'App' for the Distribution of Satellite Rainfall Estimates.
Mantas, Vasco Manuel1; Pereira, A.J.S.C.1; Liu, Zhong2
1IMAR, University of Coimbra, PORTUGAL; 2CSISS, George Mason University and NASA GES DISC, UNITED STATES
It is generally accepted that since the dawn of the so-called web 2.0  there was a significant shift in the way web services are designed. Much of the focus is now turning towards the consolidation of data and its redistribution in new formats that enable users to act on it. This change in the paradigm is also true for web services and applications that distribute scientific data.
At the same time, there is an increasing demand for timely and accurate rainfall estimates for multiple applications in a wide range of fields . The TRMM Online Visualization and Analysis System (TOVAS) became a reference in the distribution of such data, including a variety of products, some in Near Real Time [3, 4].
The capabilities offered by TOVAS, especially the output in the ASCII format, enable users to develop new, derived applications. These new applications can retrieve and redistribute or analyze the data for different time periods, regions of interest or feed the rainfall estimates into hydrographic models for instance.
Under these assumptions, a project was devised to develop a set of freely available applications and web services that can (1) simplify access from Mobile Devices to TOVAS and (2) support the development of new datasets through data repackaging and mash-up.
The mobile device application (TRMM.Mobile) was developed to run on AndroidTM and emulates the standard TOVAS access webpage but in a simplified manner. The application further simplifies data access through the storage of certain values for key parameters including the coordinates of the area of interest, start and end date.
Documentation is also developed en par with the application itself, allowing the users to learn how to use and create their own, new apps. This becomes possible because of two factors. First the increasing computing power of mobile devices coupled to a significant reduction of its cost and, secondly, because of the multiplication of free software development platforms, sometimes extremely intuitive, which transform almost anyone into a potential application developer . This approach was adopted in this component of the project, with the so-far limited release of the source project to educators and researchers, which can then build on top of the pre-existing application.
A web service was also created to access, store and resample TOVAS data and will be made available upon completion of final functionality tests. The service was written in Python and it is being hosted at Google App Engine (GAE). Named 'TRMM EXPLORE' (TRMM EXPedite ExpLoration Of Rainfall Estimates), it was designed to serve two purposes, (1) to access TOVAS data from irregular and regular grids and (2) integrate it with other data sources. The outputs are also served in different ways (visual, numerical) that can be visualized either directly by the end users in instance-specific portals, or retrieved by other, services and applications downstream. This approach enables a rapid expansion of the applications of TOVAS data, with this service acting as an intermediate.
The service also has the potential to run analysis of varying complexity on the data itself. Although initially designed for watershed monitoring it can, in reality, be applied to a broad scope of settings, like crop management for instance. The intake of data from other sources, including Land Use and Land Cover databases or surface reflectance/temperature from different Earth Observation systems is also planned for the near future. The bottom-up approach enables the multiplication of new services, often of limited direct interest to the organizations that produces the original, global datasets, but significant to small, local users. Through this multiplication of services, the development cost is transferred to the intermediate or end users and the entire process is made more efficient, even allowing new players to use the new, repackaged data in innovative ways.
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