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Modeling the hydrologically-relevant features of uncertainty of space-borne high resolution precipitation products. Preliminary Assessment for PhD. LING TANG Tennessee Technological University Department of Civil and Environmental Engineering. Outline. Introduction Research Objectives
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Preliminary Assessment for PhD
Tennessee Technological University
Department of Civil and Environmental Engineering
- One of the next generation of satellite-based Earth science missions that will study global precipitation (rain,snow and ice).
- Currently scheduled for launch in 2013.
(source from: gpm.gsfc.nasa.gov)
------( Average annual precipitation from 1961-1990 )
Ground validation data
Error metrics and error classification
Spatial interpolation of error metrics from sparse GV sites
1) TASK 1 (Years 1 and 2): Mathematical modeling of hydrologically relevant error metrics as a function of regime, season and location.
- This task will identify the total number of error metrics that are meaningful for improving the hydrologic potential of GPM and also interpretable by both user and data producing communities.
- It will begin with the initial set of nine error metrics that my advisor has already devised for a Two-Dimensional Satellite Rainfall Error Model - SREM2D (see Hossain and Anagnostou, 2006; and Hossain and Huffman, 2007-in press).
NWS Radar data
Nine error metrics in SREM2D
Simulated satellite rainfall
Aggregated simulated satellite rainfall data
Aggregated actual satellite rainfall data
- This task will seek answer to if “error is defined on the basis of GV, then how are these metrics estimated for a satellite data product without the need for extensive GV data?”
Extensive non-GV and non-PR (overpass) location
TRMM PR data (2A25)
Error metrics derived at finite PR overpass locations
Results using NWS Radar reference data(TASK 1)
- Currently involved in the creation of a 9-year (1997-2006) mosaic of National Weather Service (NWS) ground radar rainfall data (WSR-88D) over the coterminous United States. (Hydro-NEXRAD)
- I am performing statistical analysis of the rainfall distribution to understand how these parameters vary as a function of location. This part of my work is expected to help refine the exact periphery of the five zones in manner analogous to Koppen climate classification.
First one will be on the identification of distinct satellite rainfall error classification regimes in a manner similar to Koppen Classification in J. Climate. Second will identify the ideal set of error metrics through consistency analysis in J. Hydrometeorology or Water Resources Research.
- The long-term impact from my proposed project is, therefore, expected to be in laying the foundation for understanding the level to which satellite rainfall products can realistically advance societal applications (such as flood detection) in the developing world.
- My undergraduate background is in hyperspectral remote sensing of geophysical parameters such as terrain features and topography.
- My masters research focused on ‘artificial immune system’ technique for optimal extraction of features and information from remote sensing datasets.
- Ant Colony Algorithm in Image Interpretation
(China National Natural Science Fund Project)
By simulating ant colony behavior, this project aimed to establish a set of ant colony behavior optimal algorithms and models, and using them to solve image interpretation problems, mainly on aerial and satellite images.
- Artificial Immune System in Image Segmentation
This research introduced the mechanism of biological immune system into remote sensing image processing, and thus developed a refined optimal algorithm based on artificial immune system for satellite image processing such as segmentation, classification, and feature extraction.
1. Ling Tang, Zhaobao Zheng. A new approach based on artificial immune system for texture segmentation on satellite imagery. The 15th National Conference on Remote Sensing Technique. Guiyang, China. 2005
2. Ling Tang, Zhaobao Zheng . An image segmentation algorithm based on artificial immune system. Geomatics and Information Science of Wuhan University. Vol.32, No.1, Jun. 2007, 68~70.
3. Xin Yu, Zhaobao Zheng, Ling Tang. Aerial image texture classification based on a new Bayes classifier. Geomatics and Information Science of Wuhan University. Vol.31, No.2, Feb.2006 108～111.
- I have familiarized myself with some hydrologic rainfall remote sensing products.
- I have also familiarized myself the well-known Global Precipitation Climatology Project (GPCP) and satellite rainfall remote sensing.
Six years (1997-2002) of GPCP daily rainfall data (resolution of daily at 10×10) was used to analyze the spatial and temporal patterns of the global precipitation over large river basins.
- C++ (programming)
- Matlab (programming)
- ArcGIS (mapping)
- Cygwin (Unix Systems Simulator)
- ERDAS (Remote Sensing analysis)
- IDL (NASA language)