Presentation on theme: "Modeling the hydrologically-relevant features of uncertainty of space-borne high resolution precipitation products Preliminary Assessment for PhD LING."— Presentation transcript:
1Modeling the hydrologically-relevant features of uncertainty of space-borne high resolution precipitation productsPreliminary Assessment for PhDLING TANGTennessee Technological UniversityDepartment of Civil and Environmental Engineering
2Outline Introduction Research Objectives Fundamental Research HypothesisProposed Research MethodologyAnticipated Broader Impact of the Planned ProjectMy Research Background and Preparation
3Introduction The Global Precipitation Measurement (GPM) Mission - 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)
4Critical IssuesIn-situ rainfall measuring networks are on the decline. GPM will be able to demonstrate tangible benefits in hydrologic application over ungauged regions.Knowledge of satellite rainfall estimation uncertainty is required – the ParadoxScale mis-match between overland hydrologic processes that evolve at smaller and more dynamic scales (i.e., < 1 hour and < 1 km) and operational satellite precipitation forcing datasets that are available at coarser scales
5The Unresolved Paradox Representing the error structure of satellite rainfall as a function of scale against quality-controlled ground validation (GV) datasets is both a critical research problem and an application need.Satellite rainfall will be most useful over ungauged (non-GV) regions – so how can we generate uncertainty estimates for satellite data over those regions ?
6Research Objectives (Tentative) 1) To mathematically model the pertinent (hydrologically-relevant) spatio-temporal features of satellite rainfall uncertainty as a function of season and location over the US.2) To develop and test a geostatistical mapping scheme for spatial interpolation of error information from small-domain GV regions to the neighboring vast ungauged (non-GV) regions.
7MY FUNDAMENTAL RESEARCH HYPOTHESIS Hydrologists and other users, to varying degrees, need to know the errors of the satellite rainfall datasets across the range of time/space scales over the whole domain of the data set. On the other hand, satellite rainfall datasets are most useful over the vast ungauged regions of the developing world lacking in GV data. If the potential of GPM is to be unleashed over the vast regions of the developing world, then a reconciliation of this paradox is needed before the launch of GPM in 2013.
8Proposed Research Methodology Study Region and DataUnites States as the primary study region further divided into six zones (Each zone is governed by distinct rainfall climatology)
9Climate of United states ------( Average annual precipitation from )
10Temporal Resolution (hours) Study dataproductsSpatial scale(degree)Temporal Resolution (hours)Period(Years)3B40RT0.25353B41RT13B42RT3B42V69NWS(radar)0.04The primary source for GV rainfall data will comprise the National Weather Service (NWS) WSR-88D radar rainfall data.Satellite rainfall data will comprise all major products of NASA’s TMPA system (Huffman et al., 2007) – i.e., 3B40RT, 3B41RT, 3B42RT and 3B42V6.
11Ground validation data Methodology FlowchartSatellite dataGround validation dataError frameworkTask 1Task 2Error metrics and error classificationSpatial interpolation of error metrics from sparse GV sites
12Proposed Specific Tasks (Tentative) 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).
13Nine error metrics in SREM2D NWS Radar dataGVCalibrationNine error metrics in SREM2DSimulationModify error framework using various error combinationsSimulated satellite rainfallspace/time downscalingAggregated simulated satellite rainfall dataNO?Aggregated actual satellite rainfall datacomparisonYES?
142) TASK 2 (Years 2 and 3): Development and assessment of a geostatistical-based mapping scheme for spatial interpolation of error metrics from sparse GV sites.- 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?”
15Extensive non-GV and non-PR (overpass) location Surrogate referenceTRMM PR data (2A25)Error metrics derived at finite PR overpass locationsExtensive non-GV and non-PR (overpass) locationResults using NWS Radar reference data(TASK 1)ResultscomparisonYES?
16Initial work completed - Currently involved in the creation of a 9-year ( ) 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.
17Proposed Research Publications From TASK 1 –Two journal quality research papers.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.From TASK 2 – One research paper in J. Hydrometeorology on the effectivesness of spatial mapping of error metrics over ungauged regimes
18Timetable for completion of TASKS Completion DateFirstyear( )Second year( )Third year ( )TASK#1XXXTASK#2PublicationsPaper 1Paper 2Paper 3
19Anticipated Broader Impact of the Proposed Project - 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.
20My Research Background - 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.
21Background coursework Masters(Wuhan University)-Matrix Theory-Numerical Analysis-Microwave Remote Sensing-Error Processing and Reliability Theory-Theory and Technology of GIS-Image Processing and Analysis-Fundamentals of Intelligent System in Remote SensingPhD(Tennessee Tech. University)Fall 2007-CEE 6520 *Open-channel Hydraulics-CEE 6610 *Applied Environmental Chemistry-CEE 6440 HydrometeorologySpring 2008-ECE 6900 Special Problems (Intelligent Systems)-CEE 7980 Directed Study (Satellite Rainfall Uncertainty Analysis )Courses In future-CEE 6430 *Probabilistic methods (Dr. Hossain)-Three more 7000 level courses(*----core course)
22Prior Research Experience - 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 SegmentationThis 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.
23Prior Publications1. 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. 20052. 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 ～111.
24My Current Work on Hydrologic Remote Sensing at TTU - 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 ( ) 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.