Title of Presentation: An Improved Data Reduction Tool in Support of the Real-Time Assimilation of NASA Satellite Data Streams
Primary (Corresponding) Author: Rahul Ramachandran
Organization of Primary Author:
Co-Authors: Xiang Li, Sunil Movva, Sara Graves, Bradley Zavodsky, Steven Lazarus, Michael Splitt, Mike Lueken, William Lapenta
Abstract: Today’s research and operational forecast models and data assimilation systems have difficulty ingesting and utilizing large volumes of satellite data, in part due to prohibitively large computational costs, time constraints and bandwidth issues. To address this problem, NASA recently funded a project aimed at refining, testing and customizing an existing automated Intelligent Data Thinning (IDT) algorithm, developed at the
The goal of this project is to test, refine and customize the existing IDT algorithm in order to transition it into a deliverable data reduction tool useful for real-time applications with a wide variety of dense NASA satellite data streams in operational, research, and private industry communities. The project tasks include: (1) performing sensitivity analysis on IDT with selected data assimilation systems, (2) customizing IDT for use with selected multidimensional NASA satellite data sets, and (3) evaluating IDT’s performance with end-to-end analysis and numerical weather prediction model experiments. The NASA Short-term Prediction Research and Transition (SPoRT) Center, with its resident research scientists, forecast models, and real time data feeds, has been chosen as the ideal environment for operational testing of this tool. The refinements made to the IDT algorithm and the results from the sensitivity analysis will be presented.