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Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation ProductsData quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.
Document ID
20150023491
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Liu, Zhong (George Mason Univ. Manassas, VA, United States)
Heo, Gil (George Mason Univ. Manassas, VA, United States)
Date Acquired
December 23, 2015
Publication Date
December 14, 2015
Subject Category
Meteorology And Climatology
Documentation And Information Science
Mathematical And Computer Sciences (General)
Report/Patent Number
GSFC-E-DAA-TN28735
Meeting Information
Meeting: AGU Fall Meeting
Location: San Francisco, CA
Country: United States
Start Date: December 14, 2015
End Date: December 18, 2015
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: NNX15AK27A
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
precipitation
data quality
giovanni

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